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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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