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Nonparametric Statistics with Applications to Science and Engineering by Paul H Kvam
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About this book :-
Nonparametric Statistics with Applications to Science and Engineering written by
Paul Kvam
There are plenty of excellent monographs/texts dealing with nonparametric statistics, such as the encyclopedic book by Hollander and Wolfe. Nonparametrac Statzstzcal Methods. or the excellent evergreen book by Conover.
Practacal Nonparametrzc Statastacs, for example. The author used as a text the 3rd
edition of Conover's book, which is mainly concerned with what most of us
think of as traditional nonparametric statistics: proportions. ranks. categorical data. goodness of fit. and so on, with the understanding that the text
would be supplemented by the instructor's handouts. Both of us ended up
supplying an increasing number of handouts every year, for units such as density and function estimation. wavelets. Bayesian approaches to nonparametric problems. the EM algorithm. splines, machine learning, and other arguably modern nonparametric topics.
Book Detail :-
Title: Nonparametric Statistics with Applications to Science and Engineering
Edition:
Author(s): Paul H Kvam
Publisher: World Scientific
Series:
Year: 2007
Pages: 441
Type: PDF
Language: English
ISBN: 9780470081471
Country: US
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About Author :-
Paul H. Kvam, Georgia Institute of Technology, The H. hlilton Stewart School oflndustrial and Systems Engineering, Atlanta. GA
Brani Vidakovic, Georgia Institute of Technology and Emory University School of Medicine, The Wallace H. Coulter Department of Biomedical Engineering, Atlanta, GA
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Book Contents :-
Nonparametric Statistics with Applications to Science and Engineering written by
Paul Kvam
cover the following topics.
'
1. Introduction
1.1 Efficiency of Nonparametric Methods
1.2 Overconfidence Bias
1.3 Computing with MATLAB
1.4 Exercises
2. Probability Basics
2.1 Helpful Functions
2.2 Events, Probabilities and Random Variables
2.3 Numerical Characteristics of Random Variables
2.4 Discrete Distributions
2.5 Continuous Distributions
2.6 Mixture Distributions
2.7 Exponential Family of Distributions
2.8 Stochastic Inequalities
2.9 Convergence of Random Variables
2.10 Exercises
3. Statistics Basics
3.1 Estimation
3.2 Empirical Distribution Function
3.3 Statistical Tests
3.4 Exercises
References
4. Bayesian Statistics
4.1 The Bayesian Paradigm
4.2 Ingredients for Bayesian Inference
4.3 Bayesian Computation and Use of WinBUGS
4.4 Exercises
5. Order Statistics
5.1 Joint Distributions of Order Statistics
5.2 Sample Quantiles
5.3 Tolerance Intervals
5.4 Asymptotic Distributions of Order Statistics
5.5 Extreme Value Theory
5.6 Ranked Set Sampling
5.7 Exercises
6. Goodness of Fit
6.1 Kolmogorov-Smirnov Test Statistic
6.2 Smirnov Test to Compare Two Distributions
6.3 Specialized Tests
6.4 Probability Plotting
6.5 Runs Test
6.6 AIeta Analysis
6.7 Exercises
7. Rank Tests
7.1 Properties of Ranks
7.2 Sign Test
7.3 Spearman Coefficient of Rank Correlation
7.4 Wilcoxon Signed Rank Test
7.5 Wilcoxon (Two-Sample) Sum Rank Test
7.6 Mann-Whitney U Test
7.7 Test of Variances
7.8 Exercises
8. Designed Experiments
8.1 Kruskal-Wallis Test
8.2 Friedman Test
8.3 Variance Test for Several Populations
8.4 Exercises
9. Categorical Data
9.1 Chi-square and Goodness-of-Fit
9.2 Contingency Tables
9.3 Fisher Exact Test
9.4 MCNemar Test
9.5 Cochran’s Test
9.6 Mantel-Haenszel Test
9.7 CLT for Multinomial Probabilities
9.8 Simpson’s Paradox
9.9 Exercises
10. Estimating Distribution Functions
10.1 Introduction
10.2 Nonparametric Maximum Likelihood
10.3 Kaplan-Meier Estimator
10.4 Confidence Interval for F
10.5 Plug-in Principle
10.6 Semi- P ar ame tric Inference
10.7 Empirical Processes
10.8 Empirical Likelihood
10.9 Exercises
11. Density Estimation
11.1 Histogram
11.2 Kernel and Bandwidth
11.3 Exercises
12. Beyond Linear Regression
12.1 Least Squares Regression
12.2 Rank Regression
12.3 Robust Regression
12.4 Isotonic Regression
12.5 Generalized Linear Models
12.6 Exercises
13. Curve Fitting Techniques
13.1 Kernel Estimators
13.2 Nearest Neighbor Methods
13.3 Variance Estimation
13.4 Splines
13.5 Summary
13.6 Exercises
14. Wavelets
14.1 Introduction to Wavelets
14.2 How Do the Wavelets Work?
14.3 Wavelet Shrinkage
14.4 Exercises
15. Bootstrap
15.1 Bootstrap Sampling
15.2 Nonparametric Bootstrap
15.3 Bias Correction for Nonparametric Intervals
15.4 The Jackknife
15.5 Bayesian Bootstrap
15.6 Permutation Tests
15.7 More on the Bootstrap
15.8 Exercises
16. EM Algorithm
16.1 Fisher’s Example
16.2 Mixtures
16.3 EM and Order Statistics
16.4 MAP via EM
16.5 Infection Pattern Estimation
16.6 Exercises
17. Statistical Learning
17.1 Discriminant Analysis
17.2 Linear Classification Models
17.3 Nearest Neighbor Classification
17.4 Neural Networks
17.5 Binary Classification Trees
17.6 Exercises
18. Nonparametric Bayes
18.1 Dirichlet Processes
18.2 Bayesian Categorical Models
18.3 Infinitely Dimensional Problems
18.4 Exercises
A MATLAB
A.l Using MATLAB
A.2 Matrix Operations
A.3 Creating Functions in MATLAB
A.4 Importing and Exporting Data
A.5 Data Visualization
A.6 Statistics
B WinBUGS
B.l Using WinBUGS
B.2 Built-in Functions
hIATLAB Index
Author Index
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