This Book Presents Modern Nonparametric Statistics from a Practical Point of View and Emphasises Methods in Regression and Curve Fitting.DUBLIN, Ireland -- Research and Markets (http://www.researchandmarkets.com/reports/c71706) has announced the addition of "Nonparametric Statistics With Applications to Science and Engineering" to their offering.
This book presents modern nonparametric statistics from a practical point of view. It is primarily intended for use with engineers and scientists. While the book covers the necessary theorems and methods of rank tests in an applied fashion, the novelty lies in its emphasis on modern nonparametric methods in regression and curve fitting, bootstrap See boot.
(operating system, compiler) bootstrap - To load and initialise the operating system on a computer. Normally abbreviated to "boot". From the curious expression "to pull oneself up by one's bootstraps", one of the legendary feats of Baron von Munchhausen. confidence intervals, splines, wavelets See wavelet compression.
The elementary building blocks in a mathematical tool for analyzing functions. The functions can be very diverse; examples are solutions of a differential equation, and one- and two-dimensional signals. , empirical and nonparametric likelihood, and goodness of fit Goodness of fit means how well a statistical model fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e. testing. MATLAB (MATrix LABoratory) A programming language for technical computing from The MathWorks, Natick, MA (www.mathworks.com). Used for a wide variety of scientific and engineering calculations, especially for automatic control and signal processing, MATLAB runs on Windows, Mac and is the computing and programming system of choice throughout the book because of its special applicability for research analysis and simulation.
Paul H. Kvam, PhD, is Professor of Industrial and Systems Engineering at Georgia Institute of Technology Georgia Institute of Technology, in Atlanta, Ga.; coeducational; state supported; chartered 1885, opened 1888. It is a member school in the university system of Georgia. Significant among its facilities and programs are the Frank H. . His research interests include nonparametric estimation, statistical reliability with applications to engineering, and analysis of complex and dependent systems. He has written over fifty refereed articles and was named a Fellow of the American Statistical Association The American Statistical Association (ASA) is a scientific and educational society in the United States with the stated mission to promote excellence in the application of statistical science across the wealth of human endeavor. in 2006.
Brani Vidakovic, PhD, is Professor of Statistics and Director of the Centre for Bioengineering Statistics at The Wallace H. Coulter Department of Biomedical Engineering The Wallace H. Coulter Department of Biomedical Engineering is a department in the Georgia Institute of Technology's College of Engineering dedicated to the study of and research in biomedical engineering, and is named after the pioneering engineer and Georgia Tech alum Wallace H. , Georgia Institute of Technology. He has authored or co-authored three books and has published more than four dozen refereed articles. His areas of interest include wavelets, Bayesian inference, biostatistics, statistical methods in environmental research, and statistical education.
2. Probability Basics.
3. Statistics Basics.
4. Bayesian Statistics.
5. Order Statistics.
6. Goodness of Fit.
7. Rank Tests.
8. Designed Experiments.
9. Categorical Data categorical data
data relating to category such as qualitative data, e.g. dog, cat, female. It may be nominal when a name is used, e.g. location, breed, or ordinal when a range of categories is used, e.g. calf, yearling, cow. .
10. Estimating Distribution Functions.
11. Density Estimation.
12. Beyond Linear Regression.
13. Curve Fitting Techniques.
16. EM Algorithm.
17. Statistical Learning.
18. Nonparametric Bayes.
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