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The use of simplified or misspecified models: linear case.

INTRODUCTION

Chemical engineers develop simplified models (SMs) and use them for simulating, designing, controlling and optimizing many different types of processes (Brendel et al., 2006; Chang et al., 2005; Romdhane and Tizaoui, 2005; Golbert and Lewin, 2004; Lv et al., 2004; Maria, 2004; Mchaweh et al., 2004; Bagajewicz and Cabrera, 2003; Yoshida et al., 2003; Perregaard, 1993). SMs are developed when the modeller lacks an understanding of some of the underlying phenomena in complex processes, or there are insufficient data to adequately calibrate or validate an extended model (EM). An extended model is a mathematical description of the process that contains sufficient phenomenological detail to provide good predictions over the range of conditions of interest, including those experimental conditions where data may not have been collected. SMs often have reduced input and computational requirements compared with EMs, making them more portable and less expensive to use and maintain (Brooks and Tobias, 1996; Rexstad and Innis, 1985; Innis and Rexstad, 1983). SMs usually contain fewer unknown parameters, which are more readily and precisely estimated than the numerous parameters that appear in EMs. Problems of parameter estimability in EMs are more pronounced when limited experimental data are available. However, SM may fail to account for important phenomena, resulting in poor operating decisions and ill-conceived process designs. Although formal techniques have been developed for model simplification (Sun and Hahn, 2006; Kou et al., 2005a; Kou et al., 2005b; Maria, 2004; Rexstad and Innis, 1985; Innis and Rexstad, 1983), finding the right balance between simplicity and complexity usually involves more engineering judgment than science (Brooks and Tobias, 1996).

There has been considerable research in the statistics literature on the statistical consequences of using simplified or misspecified models (O'Brien et al., 2006; Waldorp et al., 2006; Waldorp et al., 2005; Rao and Wu, 2001; Bera, 2000; Golden, 1995; Miller, 1990; White, 1982, 1981; Hocking, 1976; Rosenberg and Levy, 1972; Rao, 1971; Toro-Vizcarrondo and Wallace, 1968; Wallace, 1964; Kabe, 1963; Freund et al., 1961; Goldberg, 1961; Goldberg and Jochems, 1961), particularly for models that are linear in the parameters. Three general situations have been considered: (1) input variables that belong in the true model are mistakenly omitted (this is sometimes known as undermodelling); (2) variables that have no real influence on the output variables are mistakenly included (this is sometimes known as overfitting); and (3) errors are made in the distributional assumptions of the stochastic or random component of the model (Seber and Wild, 2003; Hocking, 1976; Rao, 1971). Issues related to the third item are more difficult to generalize and are not discussed further in this paper.

Important quantitative and qualitative results have been derived to compare the parameter estimates and model predictions from misspecified models with those from correctly structured models (Miller, 1990; Abdullaev and Geidarov, 1985; Hocking, 1976; Rosenberg and Levy, 1972; Rao, 1971; Wallace, 1964; Kabe, 1963; Freund et al., 1961; Goldberg, 1961; Goldberg and Jochems, 1961). While it might seem that the use of misspecified models will always lead to inferior model predictions, and parameter estimates that are biased, this is not always true (Waldorp et al., 2006; Hocking, 1976; Rao, 1971).

Like many chemical engineers, we are particularly interested in developing phenomenological models based on material and energy balances and constitutive equations. The usual objective of the statistical approach to model building (for either empirical or mechanistic models) is to develop models that are of sufficient complexity so that the model passes statistical adequacy tests (Montgomery and Runger, 2003). When this objective has been achieved, probability statements can be assigned to the precision of the estimated parameters and model predictions (Montgomery et al., 2001; Draper and Smith, 1998). There has been far less research on providing similar information for simplified or misspecified models (Bera, 2000; Golden, 1995; White, 1982, 1981). Recent research in the use of more computationally intensive methods for statistical analysis of misspecified models (e.g. nonparametric bootstrapping) has revived interest in this topic (Waldorp et al., 2006; Fushiki, 2005; Aerts and Claeskens, 2001; Velilla, 2001; Davison and Hinkley, 1997).

In this paper we: (1) summarize the quantitative and qualitative results in the literature concerned with using simplified or misspecified models; (2) provide new insights into the conditions under which simplified or misspecified models give superior predictions compared with the correctly structured EM; and (3) evaluate methods that can be used for statistical inference for models that are simplified or misspecified. A new practical strategy, based on confidence intervals and hypothesis tests, is developed to help modellers decide whether a SM will give better predictions than the truly structured EM. However, there are considerable challenges in making such a decision. The resulting confidence intervals are quite large and the statistical tests, while exact in their construction, have poor discrimination properties for the alternative hypothesis that the SM is better or that the EM is better.

We focus on models that are linear in the parameters. While this choice might at first seem restrictive because chemical engineers tend to use non-linear models, we note that, the statistical analysis of non-linear models usually involves a linearization of the model around the nominal parameter values (Seber and Wild, 2003). Thus, the results of this paper can assist model developers in the analysis of phenomenologically based models that are non-linear in the parameters.

The paper is organized as follows. A general description of model misspecification is given in the second section. In the third section, misspecification in models that are linear in the parameters is analyzed theoretically. There are extensive results in the literature for this topic, but an important unresolved issue is the lack of practical tests for determining whether a SM will give better predictions than the truly structured EM. The new practical strategy that uses the model structure and the data available for parameter estimation is provided in the fourth section. This is followed in the fifth section by an analysis of constructive numerical methods that can be used to make statistical statements regarding the uncertainty in parameters and model predictions when the SM is used. In the sixth section, analytical results and Monte Carlo simulations from a simple example are used to provide insights into the most important results from the previous sections. The paper concludes with a brief discussion on the applicability of these methods to models that are non-linear in the parameters.

MISSPECIFIED MODELS--THE GENERAL CASE

We assume that a process can be truly described by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (1)

where [x.sub.i] is a k dimensional vector of explanatory variables for the [i.sub.th] observation, [beta] is an m dimensional vector of model parameters, and random variable [[epsilon]i] is uncorrelated and is identically distributed with mean 0 and variance [[delta].sup.2].

Typically, a modeller supposes that the observed response variable can be represented as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (2)

where [z.sub.i] is a vector of explanatory variables, [theta] is vector of parameters and g([z.sub.i], [theta]) is the function that the modeller believes (or hopes) relates ([z.sub.i], [theta]) to the response. The term ei encompasses the stochastic component and any deterministic part that is not captured by the model. The functional form of g([z.sub.i], [theta]) may be specified from a fundamental understanding of the process or a desire to find a purely empirical representation. In either case, the parameters are often estimated as the solution to the least-squares problem

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (3)

When the parameters enter the model non-linearly, a non-linear optimization algorithm is required to determine [theta]. When the parameters enter linearly, ordinary least squares (OLS) is commonly used (Montgomery and Runger, 2003).

A model-building strategy is typically iterative. A model form is postulated, and the parameters are estimated. Typically the residuals [e.sub.i] = [y.sub.i] - g([z.sub.i], [theta]) are examined for systematic patterns that suggest omitted variables. The model may also be "pruned" by eliminating parameters that are not statistically different from zero. While no assumptions on the probability structure of the stochastic components are required to determine the least-squares estimates, the validity of the statistical analysis requires several assumptions (Montgomery et al., 2001). Typically it is assumed that the model structure is correct (g([z.sub.i], [theta]) = f([x.sub.i], [beta])), that the explanatory variables are deterministic, and that [[epsilon].sub.i], in addition to being uncorrelated and identically distributed, follows a normal distribution. When these assumptions are satisfied, there is a rich body of knowledge related to model building and statistical assessment of the model (Montgomery et al., 2001; Draper and Smith, 1998).

However, there are many instances when one deliberately chooses a structural form that does not match the true process. In these instances the model may be acceptable (in an intended end-use sense), but may fail a statistical test for adequacy (Chang et al., 2005; Golbert and Lewin, 2004; Bagajewicz and Cabrera, 2003; Yoshida et al., 2003). This is particularly true when fundamental models are used. In the case of empirical models, it may happen that some of the predictor variables are deleted from the model because they are inaccessible, in which case the model is incorrect. We will refer to these structurally imperfect models as simplified or misspecified models. Several interesting questions arise:

1. Can misspecified models give better parameter estimates and model predictions than the correctly structured extended model?

2. What statistical methods can be used to analyze these models and to make statements about the quality of their predictions?

There is a rich literature that addresses question 1 (Miller, 1990; Hocking, 1976; Rao, 1971). Not surprisingly, almost all of this work relates to models that are linear in parameters. In these instances, closed-form solutions can be obtained after a definition for "better" is specified. Some results are also available to address question 2 (Waldorp et al., 2006; Waldorp et al., 2005; Bera, 2000; Golden, 1995).

MISSPECIFIED MODELS--THE LINEAR CASE

Assume that the true process is described by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (4)

where Y [member of] I[R.sup.n], [X.sub.1] [member of] I[R.sup.n xp], [X.sub.2] [member of] I[R.sup.n xp], [[beta].sub.1] [member of]I[R.sup.p], ?2 ??IRq, and [epsilon] [member of] I[R.sup.n]. We will refer to this correctly structured model as the extended model (EM).

The following assumptions are usually made (Beck and Arnold, 1977):

1. [X.sub.1] and [X.sub.2] have full column rank, and are deterministic;

2. The stochastic component [member of] is a mean zero, uncorrelated random sequence with constant variance [[sigma].sup.2].

Define ??= (?1 ?2)T, X = ([X.sub.1] [X.sub.2]). Then the OLS estimates are given by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (5)

where the subscript "E" indicates the use of the truly structured extended model.

When the OLS assumptions are satisfied,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (6)

where E(*) and

Cov(*) denote the mathematical expectation and covariance of the (*).

It is convenient to write (6) in the form of [[beta].sub.E] [([beta], [[delta].sup.2] ([X.sup.T] X).sup.-1)]. The notation (*)~([micro], [SIGMA]) denotes that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (7)

where m = p + q is the total number of parameters in the EM.

Since the model is correctly structured, OLS estimation provides the Best Linear Unbiased Estimates (BLUE) for the model parameters, in the sense that the OLS parameter estimates have the smallest variance among all unbiased estimators (Beck and Arnold, 1977). Furthermore, any linear combination of the parameter estimates of the form [a.sup.T] [[beta].sub.E] also has the smallest variance among all unbiased estimators of [a.sup.T] [beta], where a is a column vector of length m.

There are many ways in which a model that is used to represent a process can be misspecified (Hocking, 1976; Rao, 1971), including:

1. Failure to include some or all of the explanatory variables (undermodelling);

2. Inclusion of "extraneous" variables in the model (overfitting).

Even in the linear case, the true process may be complex, encompassing features such as a heteroskedastic structure for the stochastic components, or an error-in-variables structure for the explanatory variables (Beck and Arnold, 1977). The purpose of regression diagnostics is to evaluate model adequacy and reveal inadequacies.

In this paper, we focus on the consequences of undermodelling. In undermodelling, the analyst believes, or decides to use, a model of the form

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (8)

where e = [X.sub.2][[beta].sub.2] + [epsilon] is the stochastic component combined with any model mismatch. We will refer to (8) as the SM.

For the SM, only the parameters associated with the explanatory variables in [X.sub.1] are estimated by minimizing the objective function

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (9)

with respect to [epsilon], resulting in the OLS estimates [[beta].sub.is] as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (10)

where the subscript "S" indicates the use of a SM. It is readily verified that (Draper and Smith, 1998)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (11)

where [A.sub.1] [([X.sup.T.sub.1]).sub.-1] is the projection of [X.sub.1] on [X.sub.2]. These parameter estimates are generally biased, in that E ([[beta].sub.1S]) [not equal to] [[beta].sub.1], unless [A.sub.1][[beta].sub.2] = 0, which only occurs when [[beta].sub.2] = 0, or when [X.sub.1] and [X.sub.2] are orthogonal. The modeller may use the residuals from the SM to estimate the noise variance [[delta].sup.2],

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (12)

ignoring the influence of any model misspecification. The value of [s.sup.2.sub.S] is often used to construct confidence intervals for parameter estimates and model predictions and to conduct model adequacy tests. However, since [S.sup.2.sub.S] is a biased estimator of [[delta].sup.2] (Hocking, 1976), statistical tests that use [[delta].sup.2.sub.S] can be misleading.

When misspecified models are analyzed (and the modeller believes that the model is misspecified), it is common for the quality of parameter estimates (and model predictions) to be assessed using mean-squared-error (MSE) or mean-square-derrormatrix (MSEM) (Toutenburg and Trenkler, 1990; Price, 1982; Gunst and Mason, 1977; Lowerre, 1974), which account for both bias and variance. The MSEM and MSE for a parameter estimate [beta] are defined as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (13)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (14)

where [DELTA] = E([beta]) [beta] is the bias, and Tr(*) denotes the trace of the quantity (*).

Miller (1990), Hocking (1976), Rao (1971), Toro-Vizcarrondo and Wallace (1968), Wallace (1964), Kabe (1963), Freund et al. (1961), Goldberg (1961) and Goldberg and Jochems (1961) provide important results concerned with the MSE of parameter estimates and model predictions when a SM structure is selected.

Quantitative Statements Regarding Misspecification

The following results are known for misspecified models in the linear case (Hocking, 1976; Rao, 1971):

1. The omission of a variable from the EM (the truth), introduces bias and decreases the variance in all of the parameter estimates (and model predictions) obtained using the SM;

2. The MSEs of all parameter estimates (and model predictions) are decreased when a single variable in the EM is deleted whose true parameter value is smaller in magnitude than the theoretical standard deviation of its least-squares estimate;

3. The estimate of the noise variance (Equation (12)) obtained from the SM residuals is upwardly biased;

4. The inclusion of an irrelevant variable in the model increases the variance and MSEs of all of the parameter estimates (and model predictions).

Quantitative Statements Regarding Misspecification

To enable a comparison of the properties of the estimates from an EM and a SM, it is helpful to partition the expected values of the parameter estimates and their covariance matrix from the EM as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (15)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (16)

The elements of the composite covariance matrix can be obtained from sub-matrices using standard results from linear algebra (Beck and Arnold, 1977)

[A.sup.2] is the projection of [X.sub.2] on [X.sub.1]. Based on the above theoretical results, comparisons between the SM and EM can be made, both for parameter estimates and for model predictions (or other linear combinations of the parameters).

Comparison of Parameter Estimates

Table 1 summarizes the expected values, covariance matrices and MSEM of parameter estimates obtained from using the SM and the EM. The expected noise variance estimates that would be obtained are given in the final row of the table.

Mean-Based Comparison of Parameter Estimates

The parameter estimates from the correctly specified EM are unbiased. The parameter estimates from the SM are biased except when [A.sup.1] = 0, which requires that [X.sub.2] be orthogonal to [X.sub.1], a situation not likely to be encountered in practice.

Variance-Based Comparison of Parameter Estimates

From Table 1, the difference between covariance matrices for [[beta].sub1E] and [[beta].sub1S] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (18)

Since Cov ([[beta].sub2E]) = [[delta].sup.2][OMEGA], therefore [OMEGA] is positive definite, and the above difference will be positive semi-definite (Zhang, 1999). Let ?1i be the [i.sub.th] element in [[beta].sub.1] (i = 1,2, ..., p). Using properties of positive semi-definite matrices (Zhang, 1999), the following inequalities are satisfied:

Individual Parameters

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (19a)

Total Variance

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (19b)

Generalized Variance (determinant)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (19c)

Note that, the parameter estimates from the SM are biased, so the Gauss-Markov Theorem does not apply (Beck and Arnold, 1977).

In the case of overfitting, the SM would be correctly specified and the EM would contain redundant parameters, since the true value of [[beta].sub.2] is a zero vector. Both the SM and the EM would lead to unbiased parameter estimates because both the SM and the EM are correctly structured (Rao, 1971). However, the inclusion of the extraneous parameters results in less precision in the parameter estimates and in the predictions made using these parameters. In this scenario, the true covariance matrix of the parameters would be given by the entry in Table 1 under the column "Simplified Model (SM)," and the covariance matrix of the parameters for overfitting would be given by the entry in Table 1 under the column "Extended Model (EM)."

MSEM-Based Comparison of Parameter Estimates

From Table 1, the MSEM difference for [[beta].sub.1E] and [[beta].sub.1S] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (20)

This difference is positive semi-definite if

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (21)

A necessary and sufficient condition of Inequality (21) is that (Wang and Chow, 1994)

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (22)

This inequality holds when the SM gives better (in sense of smaller MSEM) parameter estimates than the EM.

Inequality (22) has several appealing interpretations. First, we note that the last entry in Table 1 can be re-arranged as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (23)

The right-hand side (RHS) of (23) is the fractional increase in the expected noise variance prediction that arises when the SM is used. If (22) holds, then

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (24)

Inequality (24) provides an upper bound on the bias of the noise variance estimate obtained from the SM when Inequality (22) is satisfied (SM parameter estimates are better than the EM estimates).

Rearranging Inequality (22), we obtain the expression for a critical ratio [R.sub.C]

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (25)

where q is the number of parameters contained in [[beta].sub.2]. Inequality (22) is then equivalent to

Examination of (26) reveals that [R.sub.C] becomes smaller (implying the SM tends to be better than the EM) in the following situations:

1. When there are high noise levels, i.e., [[delta].sup.2] is large;

2. When the true absolute values of parameters in [[beta].sub.2] are small;

3. When there are high correlations among input variable settings so that the trace of [X.sup.T.sub.2] ([I.sub.n] - [P.sub.1])[X.sub.2] is small;

4. When there are a limited number of experiments or a limited range of input conditions so that the trace of [X.sup.T.sub.2] ([I.sub.n] - [P.sub.1])[X.sub.2] is small;

Proof of statements 1 and 3 above can be found in Abdullaev and Geidarov (1985).

Comparison of Model Predictions

Model developers and users often care more about model predictions or other linear combinations of parameters than the parameter estimates themselves. Imagine that the model parameters have been estimated using a matrix of input variable settings, X [member of] I[R.sup.nxm], and then model predictions are made using other input settings Z [member of] I[R.sup.uxm] where w is the total number of predictions to be made. Z can be partitioned in the same way as X into [Z.sub.1] [member of] I[R.sup.uxp] and [Z.sub.2] I[R.sup.wxq] corresponding to the partitioned parameters in (15). Two types of model predictions can be made

1.SM predictions: [Y.sub.S] = [Z.sub.1] [[beta].sub.1S] (27a)

2.EM predictions: [Y.sub.E] = Z[[beta].sub.E] [Z.sub.1][[beta].sub.1S] + [Z.sub.2][[beta].sub.2E] (27b)

Expressions for expected values, covariance matrices and the MSEM for these predictions are shown for two cases: (1) Z = X (Table 2); and (2) Z [not equal to] X (Table 3). There is no extrapolation or interpolation in the first case, because predictions are made [a.sup.T] the same conditions under which the data were collected. The second case corresponds to a new set of conditions for which predictions are desired.

Mean-Based Comparison of Model Predictions

As seen in the first row of Tables 2 and 3, only the correctly structured EM provides unbiased model predictions.

Variance-Based Comparison of Model Predictions From Table 3, in general, the difference between covariance matrices for [Y.sub.S] and [Y.sub.E] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (28)

Since [OMEGA] is positive definite, the above difference is positive semi-definite, which means predictions from the SM cannot be more variable than those from the EM.

MSEM-Based Comparison of Model Predictions

The elements of the MSEM contain the covariances for the model predictions, plus the squared bias. From Table 3, the difference between the MSEM for [Y.sub.S] and [Y.sub.E] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (29)

Following the same argument as for (21), (22) and (26), if [R.sub.C] 1/q, then MSEM([Y.sub.S]) [less than or equal to] MSEM([Y.sub.E]). This means that when few data points are available or the data are noisy or when there are high correlations between [X.sub.1] and [X.sub.2], the SM can be expected to provide better predictions than the properly specified EM.

A special case is that, when Z = X, the MSEM difference becomes

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (30)

and the MSE difference is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (31)

In this special case, the SM is better if and only if

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (32)

Note that Inequality (32) is a necessary and sufficient condition for MSE ([Y.sub.E]) [greater than or equal to] MSE ([Y.sub.S]) and is less restrictive than (26). However, it only holds when model predictions are made using exactly the same input variable settings as those used in parameter estimation. If parameter estimation or extrapolation is the main purpose, expression (26) is more appropriate (Hocking, 1976). When there is only one variable in [X.sub.2] (q = 1), (26) and (32) become the same.

In summary, the literature on misspecified linear models provides information about the conditions under which a modeller can expect to get improved parameter estimates and model predictions when a SM is used. Unfortunately, the conditions in Inequalities (26) and (32) are based on the true values of the parameters in ?2 and on the true noise variance [[delta].sup.2]. In practical applications, the modeller does not know these true values, but can obtain estimated values, [[beta].sub.2E] and [S.sub.2E], from the data, assuming the correctly structured EM is available.

STRATEGY FOR ASSESSING UNCERTAINTY ABOUT WHICH MODEL IS BETTER

If estimated parameter values and noise variances from the EM are available, then an estimate of [R.sub.C] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (33)

If we also assume that the stochastic component [epsilon] in model (4) is Normally distributed, then based on partial F tests (Montgomery et al., 2001), [R.sub.C] follows a non-central F distribution [F.sub.q,n-m]([delta]) with q and n - m degrees of freedom. The non-centrality parameter [sigma] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (34) [[beta].sub.2E]

which equals q[R.sub.c]. When [[beta].sub.2] = 0, [R.sub.C] follows the more widely known central F distribution [F.sub.q,n-m], which is often used to test the null hypothesis that [[beta].sub.2] = 0 (Montgomery et al., 2001). Note that [[beta].sub.2E] and [S.sub.2] are obtained by fitting parameters in the extended model, so the analyst must have access to an extended model that she believes to be well structured to compute [R.sub.C] in Equation (33).

The construction of confidence intervals and hypothesis tests for many standard statistics that follow standard distributions (such as the Normal distribution, t distribution, central [chi square] distribution and central F distribution) is well established in introductory statistics textbooks. However, the construction of confidence intervals and hypothesis tests for [R.sub.C] is complicated by the fact that [[??].sub.C] follows a non-central [F.sub.q,n-m](d) distribution with unknown non-centrality parameter d = qRC. As a result, an iterative algorithm is required to find appropriate confidence intervals. The following steps (Steiger, 2004) can be used to calculate the range [[delta].sub.L], [[delta.sub.U]], the exact two-sided 100(1 - [alpha])% confidence interval for [delta].

1. Calculate the cumulative probability [p.sub.C] corresponding to [[??].sub.C] using the central F distribution with q and n - m degrees of freedom. If [p.sub.C] is less than [alpha]/2, then [[delta].sub.L] and [[delta.sub.U]] are both zero. The reason for this conclusion is that [delta] = 0 by definition, and, if we did a one-sided hypothesis test at the 100(a/2)% significance level, we could reject the null hypothesis that d is zero or larger than zero. If [p.sub.C] is less than (1 - [alpha]/2), [[delta].sub.L] = 0 and [[delta].sub.U] is calculated using Step 3. Otherwise, calculate [[delta].sub.L] and [[delta].sub.U] using Steps 2 and 3.

2. To calculate the lower limit, [[delta].sub.L], literate on the non-centrality parameter, so that the (1 - [alpha]/2) cumulative probability point (the critical value) of a non-central F distribution with q and n - m degrees of freedom equals [[??].sub.C]. This value is unique.

3. To calculate the upper limit, [[delta].sub.U], iterate on the non-centrality parameter, so that the [alpha]/2 cumulative probability point (the critical value) of a non-central F distribution with q and n - m degrees of freedom equals [[??].sub.C]. This value is unique.

Since d = q[R.sub.C], the two-sided 100(1 - [alpha])% confidence interval for [R.sub.C] is [[[delta].sub.L]/q,[[delta].sub.U]/q]. This confidence interval for [R.sub.C] contains all values of the null hypothesis that would not be rejected at the 100(1 - [alpha])% confidence level when testing the alternative hypothesis that [R.sub.C] [not equal to] k. Two values of k are of interest; k = 1/q is used to test whether we are confident that Inequality (26) is satisfied and k = 1 is used to test Inequality (32). If k [greater than or equal to] [[delta].sub.L]/q,, we can be 100(1 - [alpha]/2)% certain that [R.sub.C] > k and that the EM is better than the SM. If k = dU/q, we can be 100(1 - [alpha]/2)% certain that [R.sub.C] < k and that the SM is better than the EM. In the special case where only one parameter in the EM was not included in the SM (q = 1), Inequalities (26) and (32) become the same, and k = 1. The confidence intervals are readily computed using the cumulative non-central F distribution function in MATLAB[R] or other statistical software packages.

CONFIDENCE INTERVALS FOR PARAMETER ESTIMATES AND MODEL PREDICTIONS FROM THE SM

In situations in which the modeller has decided to use the Misspecified SM, it is desirable to obtain appropriate confidence intervals for the parameters and model predictions. Such confidence intervals rely on good estimates of variance-covariance matrices. In the literature, three methods have been proposed for estimating variance-covariance matrices for parameter estimates: (1) the conventional method, which assumes the SM is truly structured (Montgomery et al., 2001); (2) the sandwich estimator (Waldorp et al., 2006; Waldorp et al., 2005; Seber and Wild, 2003; White, 1981); and (3) nonparametric bootstrapping (Waldorp et al., 2006; Good, 2005; Martinez and Martinez, 2002; Montgomery et al., 2001; Efron and Tibshirani, 1993). The last two methods have been recommended for use under model misSpecif cation (Waldorp et al., 2006).

The conventional variance-covariance matrix of [[??].sub.1S] can be estimated by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (35)

where [s.sup.2] is an estimate of noise variance. [s.sup.2] could be determined from: (1) replicate runs (pooled variances) if replicates are available; (2) the EM ([s.sup.2.sub.E]), if the correctly structured extended model is available; (3) the SM ([s.sup.2.sub.B]); and (4) nonparametric bootstrapping ([s.sup.2.sub.B])). The conventional method for estimating the variance-covariance matrix for the parameter estimates requires the assumption that the SM is correctly structured ([[beta].sub.2] = 0). The estimated variance-covariance matrix, and the conventional confidence bounds that are determined from its diagonal elements, rely on the goodness of the noise variance estimate and the goodness of the assumption that [[beta].sub.2] = 0.

White (1981) proposed that the sandwich estimator, which is robust to model misSpecif cation, should be used to estimate the variance-covariance matrix for parameter estimates when a Misspecified model is used. The sandwich estimator is derived directly from the data without the requirement for a truly specified EM or a separate noise variance estimate from replicate experiments. The variance-covariance matrix of parameter estimates is defined as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (36)

where [[??].sub.i] is the [i.sup.th] row of [X.sub.1] and ei is the [i.sup.th] residual from the SM (i = 1, 2, ..., n). The sandwich estimator is a consistent estimator for the true variance-covariance matrix of parameter estimates even when the model is Misspecified. The sandwich estimator has been investigated for models that are non-linear in the parameters (Donaldson and Schnabel, 1987), and has been used to obtain good estimate of the Cramer-Rao bound for the use of the Wald test in situations when the model is Misspecified (Waldorp et al., 2005). Additionally, it is shown that sandwich estimator is robust against an incorrect assumption on the noise covariance (Waldorp et al., 2006; Waldorp et al., 2005).

Nonparametric bootstrapping is a computationally intensive procedure commonly used in situations when no analytical methods are available for determining confidence intervals and when the sample is representative of the population (Martinez and Martinez, 2002; Montgomery et al., 2001). The bootstrapping algorithm proceeds as follows:

1. Resample the original data ([X.sub.1], Y) B times with replacement, and for each resampled data set, estimate [[beta].sub.1] and the noise variance [[delta].sup.2];

2. The final noise variance estimate is obtained as the average of the B individual noise variance estimates (Good, 2005),

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (37)

where [s.sup.2,i]* is the [i.sup.th] noise variance estimate (i = 1, 2, ..., B), and is calculated as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (38)

3. The variance-covariance matrix for the parameter estimates is obtained directly from the parameter estimates as

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (39)

where [[??].sup.i.sub.1]* is the average of B estimates of [[beta].sub.1] (Waldorp et al., 2006).

Bootstrapping can be used to estimate the bias and to directly construct the confidence intervals for parameter estimates and model predictions. A detailed description of different algorithms and the limitations of each algorithm can be found in Efron and Tibshirani (1993). MATLAB[R] codes are available from Martinez and Martinez (2002). Chernick (1999) also described some examples from the literature when the bootstrapping method should not be used. Waldorp et al. (2006) showed that the nonparametric bootstrapping give better results than the conventional methods, especially when the noise is correlated.

ILLUSTRATIVE EXAMPLE

Theoretical Results

To illustrate the theoretical analysis provided in the third section, we consider a very simple example with three parameters in the SM (p = 3) and one additional parameter (q = 1) in the EM. Let the design matrix for the SM be [X.sub.1] = ([X.sub.11] [X.sub.12] [X.sub.13]), an orthogonal matrix (obtained from a designed experiment) containing entries of [+ or -]r. [X.sub.1]i is the [i.sup.th] column in [X.sub.1] (i = 1,2,3). We are interested in knowing whether or not the additional parameter [[beta].sub.2] in the EM should be estimated. We assume that the vector of experimental settings for the input corresponding to [[beta].sub.2] is correlated with the first column of [X.sub.1], which makes it difficult to obtain an independent estimate of [[beta].sub.2]. In our example, we can adjust the amount of correlation between [X.sub.11] and [X.sub.2] using an adjustable design factor [lambda], where 0 [less than or equal to] [lambda] [less than or equal to] 1. We let [X.sub.2] = [lambda] [X.sub.11] + [square root of] 1 - [[lambda].sup2] W where W is a (n x 1) vector with entries of [+ or -]r, which is orthogonal to all columns in [X.sub.1]. Note that [X.sub.2] is correlated with the first column of [X.sub.1], but not with the other columns. We will consider different experimental designs, corresponding to different values of [+ or -]r When [lambda] = 0, [X.sub.11] and [X.sub.2] are uncorrelated, and when [lambda] = 1, [X.sub.11] = [X.sub.2].

For instance, when n = 16 data points are used and the input range for all independent variables is r = 1, the input settings are

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (40)

where [W.sup.T] = (-1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 1 1)T.

The EM is described by

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (41)

where [[beta].sub.1i] denotes the [i.sup.th] parameter in [[beta].sub.1] , and e is independently and identically distributed with mean 0 and variance [[sigma].sup.2] following a Normal distribution. We selected this example because it can readily be used to study the influence of various parameters (e.g. correlation in the experimental design, input range and number of data points) on whether the SM or the EM will give better predictions. The covariance matrix of the parameter estimates obtained using the EM is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (42)

As [lambda] [right arrow] 1, [[delta].sup.2]/(n[r.sup.2](1-.2)), which is the variance of [[[??].sub.11E] and of [[[??].sub.2E], increases dramatically, and the correlation between [[??].sub.11E] and [[??].sub.2E] approaches -1. However, since [X.sub.2] is orth

The SM is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (43)

The expected values of the parameter estimates (from (11)) obtained using the SM are

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (44)

It can be seen that [[??].sub.11S] will be biased unless [lambda] = 0 or [[beta].sub.2] = 0. [[??].sub.12S] and [[??].sub.13S] are unbiased. The covariance matrix of [[??].sub.15] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (45)

Comparing with (42), it is seen that

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (46)

These two variances are equal only when there is no correlation between [X.sub.11] and [X.sub.2] ([lambda] = 0).

Based on the results summarized in Tables 1 and 2, the MSE of parameter estimates and model predictions from the SM and the EM are

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (47)

From the MSE expressions given in (47),

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (48)

The combinations of ([[sigma].sup.2], n, r, [lambda], [[beta].sub.2]) that satisfy Inequalities (26) and (32) are very clear. [R.sub.C] is small when: (1) [[sigma].sup.2] is large (high noise levels); (2) n is small (few data points); (3) r is small (small range of input conditions); (4) [lambda] [right arrow] 1 (strong correlation among input variables in the SM with the remaining variables in the EM); and (5) [[beta].sup.2.sub.2] 2 is small (small values for the excluded parameters). This example will be used in Monte Carlo simulations described below.

Monte Carlo Simulations

In this section, Monte Carlo simulations are performed to illustrate the theoretical analysis in the previous subsection. Note that, since there is only one additional parameter in the EM (q = 1), Inequalities (26) and (32) are the same.

We cmonsider a set of experiments with n = 16 data points, and the input range r = 1, as described in (40) of earlier. Let the true parameter values be

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (49

and the noise variance [[sigma].sup.2] = 1. For this example, [R.sub.C] is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.] (50)

Based on the above expression, the range of [lambda] values that ensures that [R.sub.C] [less than or equal to] 1 is 0.968 [less than or equal to] [lambda] [less than or equal to] 1. If [lambda] is in this interval, the SM is better than the EM in the sense of MSE for parameter estimates and model predictions. To numerically demonstrate several of the concepts, let [lambda] = 0.99, which gives [R.sub.C] = 0.3184.

Comparison of Parameter Estimates and Model Predictions from the SM and EM

A total of 1000 simulated data sets were generated using different random noise sequences. Figure 1 shows a boxplot comparison of parameter estimates from the SM and the EM.

[[??].sub.11S] has much smaller variance than [[??].sub.11E] due to the strong correlation between [X.sub.2] and [X.sub.11], but [[??].sub.11S] is biased. Point estimates of parameters [[beta].sub.12] and [[beta].sub.13] from both models are the same, because [X.sub.2] is orthogonal to [X.sub.12] and [X.sub.13].

Figure 2 shows the model predictions made at the 1st, 5th, 11th and 16th observation point (model predictions made at other observation points have similar behaviour). Model predictions from the EM have larger variances than those from the SM, which are biased. As expected, predictions from the SM are better, on the average, than those from the EM.

Deciding Whether to Use the SM or the EM

To illustrate the construction of confidence intervals for [R.sub.C] using available data, we will consider two cases (i.e., [[[??].sub.C] = 0.6585 and [[[??].sub.C] = 1.5821 could be obtained from different simulated data sets) using the model described in the previous subsection. Recall that the true value is [R.sub.C] = 0.3184. The first value, [[[??].sub.C] = 0.6585 corresponds to the median value from the probability density function for [[[??].sub.C](which is distributed as F1,12(d) with d = q[R.sub.C] = 0.3184). The second value, [[[??].sub.C] = 1.5821, corresponds to the mean of the same distribution. The large difference between median and mean indicates that the distribution is right skewed. Using the algorithm described in the section entitled "Strategy for Assessing Uncertainty About Which Model is Better", the two-sided 100(1 - [alpha])% confidence interval for [R.sub.C] is constructed for different values of a. When [[[??].sub.C] = 0.6585, the upper and lower limits are plotted in Figure 3. As a increases, the confidence limits narrow and converge to [[[??].sub.C] = 0.6585. The limits are very wide for typical values of a that are commonly recommended for confidence intervals (i.e., a = 0.1). Although [R.sub.C] = 0.3184 < 1, which indicates that the SM is better than the EM, the results in Figure 3 show that, for a reasonable value of a (near 0.10), the two-sided confidence interval for [R.sub.C] is [0, 6].

Since 1.0 is within this range, we are unable to distinguish whether the SM gives better predictions than the EM (this is a Type II error).

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

The confidence limits obtained from the mean value of [[[??].sub.C] = 1.5821 are shown in Figure 4. For a near 0.10, the two-sided confidence interval is [0, 8.5], which is broader than in Figure 3. The confidence intervals in Figures 3 and 4 are exact (Steiger, 2004). The high probability of mistakenly accepting the null hypothesis (concluding that the SM is not significantly better than the EM) arises from the limited data (few data points, limited input range and correlated design). In situations where there are more data points or there are more parameters in the EM (q >1), the confidence intervals for [R.sub.C] become narrower (higher degrees of freedom in the non-central F distribution). Narrower confidence intervals lead to better discrimination about which model is better. We are currently investigating the power of statistical tests to determine whether the EM or the SM is preferred using larger and more realistic models and data sets of interest to chemical engineers.

Statistical Inference based on the SM

In this section, we compare various estimates for the variance of parameter estimates from the SM and the additive noise e. Two cases are considered: (1) [lambda] = 0.90, which corresponds to [R.sub.C] = 3.04 > 1, so that the EM is better; and (2) [lambda] = 0.99, which corresponds to [R.sub.C] = 0.3184 < 1, so that the SM is better (on average).

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

As there are no replicate experiments available in this example, we only consider using [s.sup.S.sub.2] (from the SM residuals), [s.sup.E.sub.2] (from the EM residuals) and [s.sup.B.sub.2] (from nonparametric bootstrapping) to estimate the noise variance [[sigma].sup.2]. To evaluate these three methods, a total of 1000 data sets were generated using different random noise sequences. For each simulated data set, B = 200 bootstraps were performed. In all simulations, we assumed that the noise is independently and identically distributed following a standard Normal distribution. The estimated noise variances from each situation ([lambda] = 0.90 and [lambda] = 0.99) are compared using boxplots in Figure 5. The dashed line is the true value of the noise variance used in the simulation ([[sigma].sup.2] = 1). It is seen that, in the case when the EM is better, [s.sup.S.sub.2]is biased upward, and [s.sup.E.sub.2] and [s.sup.B.sub.2] provide good estimate of [[sigma].sup.2]. However, in the case when the SM is better, [s.sup.S.sub.2] and [s.sup.E.sub.2] are good estimates of [[sigma].sup.2]. Based on these simulations, it seems that, when the correctly structured EM is available, it should be used to estimate the noise variance because it provides an unbiased estimate. However, this issue requires further investigation because bias is not the entire problem. Perhaps other variance estimators will provide estimates with lower mean-squared-error.

The estimated variance of [[[??].sub.11S] from the SM could be obtained by three methods: (1) conventional methods (Equation (35) with [s.sup.S.sub.2] (SM) or [[[??].sub.11S] [s.sup.E.sub.2] (EM)); (2) sandwich estimator (Equation (36)); and (3) nonparametric bootstrapping (Equation (39)). The results from each situation (for both [lambda] = 0.90 and [lambda] = 0.99) are compared in Figure 6. Since and [[[??].sub.13S] are the same as those obtained using the EM (Figure 1), we do not consider them in this section.

It is seen that, for our example, all the methods give similar results. Several nonparametric bootstrapping algorithms can be directly used to construct the confidence intervals for parameter estimates. For this particular example, there is no noticeable difference between the results obtained using the algorithms described by Efron and Tibshirani (1993) and by Martinez and Martinez (2002).

[FIGURE 5 OMITTED]

CONCLUSIONS AND RECOMMENDATIONS

In this paper, a number of important issues related to the use of simplified or Misspecified models have been reviewed. Much of the statistical literature has focused on model validation and model use for models that are assumed to be statistically valid. There are however many instances when it is not possible to construct a model that is deemed statistically acceptable. There are other instances where the use of a truly structured extended model is undesirable due to the inherent complexity of the model. Additionally, there are instances where simplified or Misspecified models can give predictions that are superior, in the mean-squared-error sense, to those from the extended model.

For models that are linear in the parameters, it is possible to study the implications of using simplified or extended models. This study was undertaken using both theoretical analysis and Monte Carlo simulations. The simplified model gives better parameter estimates and model predictions (on average) than the extended model if the inequalities (involving the critical ratio [R.sub.C]) described in (26) and (32) are satisfied. In these situations, the simplified model is superior, even though the extended model is correctly structured and the simplified model is Misspecified. It was demonstrated that these inequalities are satisfied when there are high noise levels, strong correlations among input variables, small number of experiments, a small range of independent variable settings, or small true values for parameters that are excluded from the simplified model. All of these situations are unfavourable for obtaining precise parameter estimates. When modellers are faced with uninformative and noisy data from poorly designed experiments, they should not try to estimate too many model parameters. Rather, they should confine themselves to fitting only a few key parameters that appear in the most important parts of their models. Unfortunately, there are considerable challenges in deciding which model is preferred, using the limited data that are available for parameter estimation. We have demonstrated how confidence intervals can be constructed for the critical ratio [R.sub.C]. These intervals are often quite wide, especially when the number of parameters excluded is small. The result is that the statistical tests, while exact in their construction, can have poor discrimination properties for alternative hypotheses (either that the simplified model is better or that the extended model is better) when the data are uninformative. However, when the data are informative, and the terms left out of the simplified model are important, the lower confidence bound for [R.sub.C] becomes greater than 1, and firm conclusions can be drawn that the extended model is better.

[FIGURE 6 OMITTED]

Several methods were investigated for estimating the noise variance and variance-covariance matrix of the estimated parameters obtained from the simplified or Misspecified model. Interest in this area has been revived by the availability of inexpensive computing for computationally intensive methods such as the nonparametric bootstrapping.

The focus in this paper was on models that are linear in the parameters, but the results are very important for phenomenologically based models that are non-linear in the parameters. In these instances, there are often competing models that can be used. The difference in complexity between a simplified model and an extended model can be substantial. The application of the results in this paper can be used, in the first instance, on the linearized representation of the non-linear model. In these instances, X is replaced by a parametric sensitivity matrix, whose [i.sup.th] column is [delta]f(X, [beta])/[delta][[beta].sub.i]|[beta]=[[??], where [[??] is either a least-squares estimate of [beta] or an initial guess for the parameter values. In non-linear models, the parametric sensitivity matrix, which corresponds to XT X for a linear model, is often ill-conditioned (Kou et al., 2005a, b; Bates and Watts, 1988), so that conditions under which the simplified model gives superior predictions are often present. Our future work will involve deciding how to simplify non-linear models so that the best possible predictions and parameter estimates can be obtained using limited data.
NOMENCLATURE

a vector of coefficients
e stochastic component
f, g functions relating explanatory variables, parameters
 to response variable
k number of explanatory variables in the true model
m total number of parameters
n number of observations
p number of parameters in first part
[p.sub.C] cumulative probability of [[??].sub.C] based on
 central F distribution
q number of parameters in second part
r input range
[s.sup.2] sample variance
w total number of predictions
x, z single observation of explanatory variable
[[[??].sub.i] [i.sup.th] row of [X.sub.1]
y single observation of response variable
A auxiliary regression matrix
B number of bootstraps
I identity matrix
P projection matrix
[R.sub.C] critical value
S sum square residuals
W vector of length n with entries of [+ or -]r
X matrix of regression variables
Y response variables
Z matrix of prediction variables

Greek Symbols

[alpha] significance level
[beta], [theta] unknown parameters
[delta] non-centrality parameter
[epsilon] stochastic component
[lambda] correlation factor
[micro] expectation
[[sigma.sup.2] unknown noise variance
[GAMMA] variance-covariance matrix of parameter estimates in
 the first part
[DELTA] bias
[SIGMA] variance-covariance
[PSI] covariance matrix between the first part and the
 second part
[OMEGA] variance-covariance matrix of parameter estimates in
 the second part

Superscripts

-1 inverse
T transpose
^ estimated value
- mean value

Subscripts

1 first partitioned part
2 second partitioned part
i index
BOOT results from nonparametric bootstrapping
CONV results from conventional methods
E extended model
L lower confidence limit
S simplified model
SANW results from sandwich estimator
U upper confidence limit
* results from nonparametric bootstrapping

Abbreviations

min minimization
Cov covariance matrix
E mathematical expectation
EM correctly structured extended model
MSE mean-squared-error
MSEM mean-squared-error-matrix
OLS ordinary least-squares
RHS right-hand side
SM simplified/Misspecified model
Tr trace
Var variance

Others

I[R.sup.n] column vector of length n taking real values
I[R.sup.nxm] (n x m) matrix taking real values


Manuscript received February 3, 2007; revised manuscript received May 1, 2007; accepted for publication May 3, 2007.

REFERENCES

Abdullaev, F. M. and E. K. Geidarov, "A Recursive 2-Step Method of Least-Squares," Automat. Rem. Contr. 46(1), 66-72 (1985).

Aerts, M. and G. Claeskens, "Bootstrap Tests for Misspecified Models, with Application to Clustered Binary Data," Comput. Stat. Data An. 36(3), 383-401 (2001).

Bagajewicz, M. J. and E. Cabrera, "Data Reconciliation in Gas Pipeline Systems," Ind. Eng. Chem. Res. 42(22), 5596-5606 (2003).

Bates, D. M. and D. G. Watts, "Nonlinear Regression Analysis and Its Applications," John Wiley & Sons, NY (1988).

Beck, J. V. and K. J. Arnold, "Parameter Estimation in Engineering and Science," John Wiley & Sons, NY (1977).

Bera, A. K., "Hypothesis Testing in the 20th Century with a Special Reference to Testing with Misspecified Models," in "Statistics for the 21st Century: Methodologies for Applications of the Future," C. R. Rao, G. J. Szekely, Eds., Marcel Dekkar, NY (2000), pp. 33-92.

Brendel, M., D. Bonvin and W. Marquardt, "Incremental Identification of Kinetic Models for Homogeneous Reaction Systems," Chem. Eng. Sci. 61, 5404-5420 (2006).

Brooks, R. J. and A. M. Tobias, "Choosing the Best Model: Level of Detail, Complexity, and Model Performance," Math. Comput. Model. 24(4), 1-14 (1996).

Chang, S., T. D. Waite and A. G. Fane, "A simplified Model for Trace Organics Removal by Continuous Flow PAC Adsorption/Submerged Membrane Processes," J. Membrane Sci. 253(1-2), 81-87 (2005).

Chernick, M. R., "Bootstrap Methods: A Practitioner's Guide," John Wiley & Sons, NY (1999).

Davison, A. C. and D. V. Hinkley, "Bootstrap Methods and Their Applications," Cambridge University Press, U.S.A. (1997).

Donaldson, J. R. and R. B. Schnabel, "Computational Experience with confidence Regions and confidence Intervals for Nonlinear Least Squares," Technometrics 29(1), 67-82 (1987).

Draper, N. R. and H. Smith, "Applied Regression Analysis," 3rd ed, John Wiley & Sons, NY (1998).

Efron, B. and R. J. Tibshirani, "An Introduction to the Bootstrap," Chapman and Hall, London (1993).

Freund, R. J., C. W. Cluniesross and R. W. Vail, "Residual Analysis," J. Am. Stat. Assoc. 56(293), 98-104 (1961).

Fushiki, T., "Bootstrap Prediction and Bayesian Prediction under Misspecified Models," Bernoulli 11(4), 747-758 (2005).

Golbert, J. and D. R. Lewin, "Model-Based Control of Fuel Cells: (1) Regulatory Control," J. Power Sources 135(1-2), 135-151 (2004).

Golden, R. M., "Making Correct Statistical Inferences Using a Wrong Probability Model," J. Math. Psychol. 39, 3-20 (1995).

Goldberg, A., "Stepwise Least-Squares--Residual Analysis and Specif cation Error," J. Am. Stat. Assoc. 56(296), 998-1000 (1961).

Goldberg, A. and D. B. Jochems, "Note on Stepwise Least-Squares," J. Am. Stat. Assoc. 56(293), 105-110 (1961).

Good, P. I., "Resampling Methods: A Practical Guide to Data Analysis," 3rd ed, Birkhauser, US (2005)

Gunst, R. F. and R. L. Mason, "Biased Estimation in Regression--Evaluation using Mean Squared Error," J. Am. Stat. Assoc. 72(359), 616-628 (1977).

Hocking, R. R., "Analysis and Selection of Variables in Linear Regression," Biometrics 32(1), 1-49 (1976).

Innis, G. and E. Rexstad, "Simulation Model Simplification Techniques," Simulation 41(1), 7-15 (1983).

Kabe, D. G., "Stepwise Multivariate Linear-Regression," J. Am. Stat. Assoc. 58(303), 770-773 (1963).

Kou, B., K. B. McAuley, C. C. Hsu and D. W. Bacon, "Mathematical Model and Parameter Estimation for GasPhase Ethylene/Hexene Copolymerization with Metallocene Catalyst," Macromol. Mater. Eng. 290(6), 537-557 (2005a).

Kou, B., K. B. McAuley, C. C. Hsu, D. W. Bacon and K. Z. Yao, "Mathematical Model and Parameter Estimation for GasPhase Ethylene Homopolymerization with Supported Metallocene Catalyst," Ind. Eng. Chem. Res. 44(8), 2428-2442 (2005b).

Lowerre, J. M., "Mean-Square Error of Parameter Estimates for Some Biased Estimators," Technometrics 16(3), 461-464 (1974).

Lv, P., J. Chang, T. Wang, C. Wu and N. Tsubaki, "A Kinetic Study on Biomass Fast Catalytic Pyrolysis," Energ. Fuel. 18(6), 1865-1869 (2004).

Maria, G., "A Review of Algorithms and Trends in Kinetic Model Identification for Chemical and Biochemical Systems," Chem. Biochem. Eng. Q. 18(3), 195-222 (2004).

Martinez, W. L. and A. R. Martinez, "Computational Statistics Handbook with MATLAB," Champman & Hall/CRC, U.S.A. (2002).

Mchaweh, A., A. Alsaygh, K. Nasrifar and M. Moshfeghian, "A simplified Method for Calculating Saturated Liquid Densities," Fluid Phase Equilib. 224(2), 157-167 (2004).

Miller, A. J., "Subset Selection in Regression," Chapman and Hall, London (1990).

Montgomery, D. C., E. A. Peck and G. G. Vining, "Introduction to Linear Regression Analysis," 3rd ed, John Wiley & Sons, NY (2001).

Montgomery, D. C. and G. C. Runger, "Applied Statistics and Probability for Engineers," 3rd ed, John Wiley & Sons, NY (2003).

O'Brien, S. M., L. L. Kupper and D. B. Dunson, "Performance of Tests of Association in Misspecified Generalized Linear Models," J. Stat. Plan. and Infer. 136, 3090-3100 (2006).

Perregaard, J., "Model Simplification and Reduction for Simulation and Optimization of Chemical Processes," Comput. Chem. Eng. 17(5-6), 465-483 (1993).

Price, J. M., "Comparisons among Regression--Estimators under the Generalized Mean-Square Error Criterion," Commun. Stat.-Theor. M. 11(17), 1965-1984 (1982).

Rao, P., "Some Notes on MisSpecif cation in Multiple Regressions," Am. Stat. 25(5), 37-39 (1971).

Rao, C. R. and Y. Wu, "On Model Selection," in "Model Selection," P. Lahiri, Institute of Mathematical Statistics, Beachwood, OH (2001), pp. 1-64.

Rexstad, E. and G. S. Innis, "Model Simplification--3 Applications," Ecol. Model. 27(1-2), 1-13 (1985).

Romdhane, M. and C. Tizaoui, "The Kinetic Modelling of a Steam Distillation Unit for the Extraction of Aniseed (Pimpinelia Anisum) Essential Oil," J. Chem. Technol. Biot. 80(7), 759-766 (2005).

Rosenberg, S. H. and P. S. Levy, "Characterization on MisSpecif cation in General Linear Regression Model," Biometrics 28(4), 1129-1133 (1972).

Seber, G. A. F. and C. J. Wild, "Nonlinear Regression," John Wiley & Sons, NJ (2003).

Steiger, J. H., "Beyond the F Test: Effect Size confidence Intervals and Tests fo Close Fit in the Analysis of Variance and Contrast Analysis," Psychol. Methods 9(2), 164-182 (2004).

Sun, C. and J. Hahn, "Parameter Reduction for Stable Dynamical Systems based on Hankel Singular Values and Sensitivity Analysis," Chem. Eng. Sci. 61, 5393-5403 (2006)

Toro-Vizcarrondo, C. and T. D. Wallace, "A Test of the Mean Square Error Criterion for Restrictions in Linear Regression," J. Am. Stat. Assoc. 63(322), 558-572 (1968).

Toutenburg, H. and G. Trenkler, "Mean-Square Error Matrix Comparisons of Optimal and Classical Predictors and Estimators in Linear-Regression," Comput. Stat. Data An. 10(3), 297-305 (1990).

Velilla, S., "On the Bootstrap in Misspecified Regression Models," Comput. Stat. Data An. 36(2), 227-242 (2001).

Waldorp, L. J., R. P. P. P. Grasman and H. M. Huizenga, "Goodness-of-Fit and confidence Intervals of Approximate Models," J. Math. Psychol. 50, 203-213 (2006).

Waldorp, L. J., H. M. Huizenga and R. P. P. P. Grasman, "The Wald Test and Cramer-Rao Bound for Misspecified Models in Electromagnetic Source Analysis," IEEE T. Signal Proces 53(9), 3427-3435 (2005).

Wallace, T. D., "Efficiencies for Stepwise Regressions," J. Am. Stat. Assoc. 59(308), 1179-1182 (1964).

Wang, S. G. and S. C. Chow, "Advanced Linear Models: Theory and Applications," Marcel Dekker, NY (1994).

White, H., "Maximum Likelihood Estimation of Misspecified Models," Econometrica 50(1), 1-26 (1982).

White, H., "Consequences and Detection of Misspecified Nonlinear Regression Models," J. Am. Stat. Assoc. 76(374), 419-433 (1981).

Yoshida, H., Y. Takahashi and M. Terashima, "A simplified Reaction Model for Production of Oil, Amino Acids, and Organic Acids from Fish Meat by Hydrolysis under Sub-Critical and Supercritical Conditions," J. Chem. Eng. Japan 36(4), 441-448 (2003).

Zhang, F., "Matrix Theory: Basic Results and Techniques," Springer-Verlag, NY (1999).
Table 1. Comparison of parameter estimates and variance estimates from
EM and SM

 Extended Model (EM)

E([[beta].sub.1]) [[beta].sub.1]
E([[beta].sub.2]) [[beta].sub.2]
Cov([[beta].sub.1]) [[sigma].sup.2]
 [([X.sup.T.sub.1][X.sub.1]).sup.-1] +
 [[sigma].sup.2][A.sub.1][([X.sup.T.sub.2]
 ([I.sub.n]-[P.sub.1])[X.sub.2]).sup.-1]
 [A.sup.T.sub.1]
Cov([[beta].sub.2]) [[sigma].sup.2][([X.sup.T.sub.2]
 ([I.sub.n]-[P.sub.1])[X.sub.2]).sup.-1]
MSEM([[beta].sub.1]) [[sigma].sup.2]
 [([X.sup.T.sub.1][X.sub.1]).sup.-1] +
 [[sigma].sup.2][A.sub.1][([X.sup.T.sub.2]
 ([I.sub.n]-[P.sub.1])[X.sub.2]).sup.-1]
 [A.sup.T.sub.1]
E([s.sup.2]) [[sigma].sup.2]

 Simplified Model (SM)

E([[beta].sub.1]) [[beta].sub.1] + [A.sub.1][[beta].sub.2]
E([[beta].sub.2])
Cov([[beta].sub.1]) [[sigma].sup.2]
 [([X.sup.T.sub.1][X.sub.1]).sup.-1]
Cov([[beta].sub.2])
MSEM([[beta].sub.1]) [[sigma].sup.2]
 [([X.sup.T.sub.1][X.sub.1]).sup.-1] +
 [A.sub.1][[beta].sub.2][[beta].sup.T.sub.2]
 [A.sup.T.sub.1]
E([s.sup.2]) [[sigma].sup.2] + [[beta].sup.T.sub.2]
 [X.sup.T.sub.2] ([I.sub.n] - [P.sub.1])
 [X.sub.2][[beta].sub.2]/ n - p

Table 2. Model predictions when Z = X

 SM Prediction [Y.sub.S]

E(Y) [X.sub.1][[beta].sub.1] + [X.sub.1][A.sub.1][[beta].sub.2]
Cov(Y) [[sigma].sup.2][P.sub.1]
MSEM(Y) [[sigma].sup.2][P.sub.1] + ([I.sub.n] - [P.sub.1])
 [X.sub.2][[beta].sub.2][[beta].sup.T.sub.2][X.sup.T.sub.2]
 ([I.sub.n] - [P.sub.1])

 EM Prediction [Y.sub.E]

E(Y) [X.sub.1][[beta].sub.1] + [X.sub.2][[beta].sub.2]
Cov(Y) [[sigma].sup.2][P.sub.1] + [[sigma].sup.2]
 ([I.sub.n] - [P.sub.1])[X.sub.2][OMEGA][X.sup.T.sub.2]
 ([I.sub.n] - [P.sub.1])
MSEM(Y) [[sigma].sup.2][P.sub.1] +
 [[sigma].sup.2]([I.sub.n] - [P.sub.1])[X.sub.2][OMEGA]
 [X.sup.T.sub.2]([I.sub.n] - [P.sub.1])

Table 3. Model predictions when Z [not equal to] X

 SM Prediction [Y.sub.S]

E(Y) [Z.sub.1][[beta].sub.1] + [Z.sub.1][A.sub.1][[beta].sub.2]
Cov(Y) [[sigma].sup.2][Z.sub.1][([X.sup.T.sub.1][X.sub.1]).sup.-1]
 [Z.sup.T.sub.1]
MSEM(Y) [[sigma].sup.2][Z.sub.1][([X.sup.T.sub.1][X.sub.1]).sup.-1]
 [Z.sup.T.sub.1] +
 ([Z.sub.1][A.sub.1] - [Z.sub.2])[[beta].sub.2]
 [[beta].sup.T.sub.2][([Z.sub.1][A.sub.1] - [Z.sub.2]).sup.T]

 EM Prediction [Y.sub.E]

E(Y) [Z.sub.1][[beta].sub.1] + [Z.sub.2][[beta].sub.2]
Cov(Y) [[sigma].sup.2][Z.sub.1][([X.sup.T.sub.1][X.sub.1]).sup.-1]
 [Z.sup.T.sub.1] +
 [[sigma].sup.2]([Z.sub.1][A.sub.1] - [Z.sub.2])[OMEGA]
 [([Z.sub.1][A.sub.1] - [Z.sub.2]).sup.T]
MSEM(Y) [[sigma].sup.2][Z.sub.1][([X.sup.T.sub.1][X.sub.1]).sup.-1]
 [Z.sup.T.sub.1] +
 [[sigma].sup.2]([Z.sub.1][A.sub.1] - [Z.sub.2])[OMEGA]
 [([Z.sub.1][A.sub.1] - [Z.sub.2]).sup.T]
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Author:Wu, Shaohua; Harris, T.J.; McAuley, K.B.
Publication:Canadian Journal of Chemical Engineering
Date:Aug 1, 2007
Words:10384
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