ISBN13: 978-0534387754

ISBN10: 0534387756

Cover type:

Edition/Copyright: 04

Publisher: Brooks/Cole Publishing Co.

Published: 2004

International: No

ISBN10: 0534387756

Cover type:

Edition/Copyright: 04

Publisher: Brooks/Cole Publishing Co.

Published: 2004

International: No

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Guided by problems that frequently arise in actual practice, James Higgins? book presents a wide array of nonparametric methods of data analysis that researchers will find useful. It discusses a variety of nonparametric methods and, wherever possible, stresses the connection between methods. For instance, rank tests are introduced as special cases of permutation tests applied to ranks. The author provides coverage of topics not often found in nonparametric textbooks, including procedures for multivariate data, multiple regression, multi-factor analysis of variance, survival data, and curve smoothing. This truly modern approach teaches non-majors how to analyze and interpret data with nonparametric procedures using today?s computing technology.

**Benefits: **

- The author provides a narrative presentation of very technical material, which helps non-statisticians understand difficult concepts.
- Many examples and exercises use real data.
- Selected computer output and code show how methods are implemented. Three statistical packages are featured: StatXact, Resampling Stats, and S-Plus.
- This book presents a comprehensive and accessible compilation of modern nonparametric techniques not often found in a single book, but rather in individual books on traditional nonparametric rank tests, permutations tests, bootstrapping procedures, nonparametric survival analysis, etc.
- Current references to the latest literature on each topic are included.

1 ONE-SAMPLE METHODS

Preliminaries

A Nonparametric Test and Confidence Interval for the Median

Estimating the Population CDF and Quantiles

A Comparison of Statistical Tests

2 TWO-SAMPLE METHODS

A Two-Sample Permutation Test

Permutation Tests Based on the Median and Trimmed Means

Random Sampling the Permutations

Wilcoxon Rank-Sum Test

Wilcoxon Rank-Sum Test Adjusted for Ties

Mann-Whitney Test and a Confidence Interval

Scoring Systems

Test for Equality of Scale Parameters and an Omnibus Test

Selecting Among Two-Sample Tests

Large Sample Approximations

Exercises

3 K-SAMPLE METHODS

K-Sample Permutation Tests

The Kruskal-Wallis Test

Multiple Comparisons

Ordered Alternatives

Exercises

4 PAIRED COMPARISONS AND BLOCKED DESIGNS

Paired Comparison Permutation Test

Signed-Rank Test

Other Paired-Comparison Tests

A Permutation Test for a Randomized Complete Block Design

Friedman's Test for a Randomized Complete Block Design

Ordered Alternatives for a Randomized Complete Block Design

Exercises

5 TESTS FOR TRENDS AND ASSOCIATION

A Permutation Test for Correlation and Slope

Spearman Rank Correlation

Kendall's Tau

Permutation Tests for Contingency Tables

Fisher's Exact Test for a 2 X 2 Contingency Table

Contingency Tables With Ordered Categories

Mantel-Haenszel Test

Exercises

6 MULTIVARIATE TESTS

Two-Sample Multivariate Permutation Tests

Two-Sample Multivariate Rank Tests

Multivariate Paired Comparisons

Multivariate Rank Tests for Paired Comparisons

Multi-response Categorical Data

Exercises

7 ANALYSIS OF CENSORED DATA

Estimating the Survival Function

Permutation Tests for Two-Sample Censored Data

Gehan's Generalization of the Mann-Whitney-Wilcoxon Test

Scoring Systems for Censored Data

Tests Using Scoring Systems for Censored Data

Exercises

8 NONPARAMETRIC BOOTSTRAP METHODS

The Basic Bootstrap Method

Bootstrap Intervals for Location-Scale Models

BCA and Other Bootstrap Intervals

Correlation and Regression

Two-Sample Inference

Bootstrap Sampling from Several Populations

Bootstrap Sampling for Multiple Regression

Multivariate Bootstrap Sampling

Exercises

9 MULTIFACTOR EXPERIMENTS

Analysis of Variance Models

Aligned Rank Transform

Testing for Lattice-Ordered Alternatives

Exercises

10 SMOOTHING METHODS AND ROBUST MODEL FITTING

Estimating the Probability Density Function

Nonparametric Curve Smoothing

Robust and Rank-Based Regression

Exercises

TABLES

REFERENCES

ISBN10: 0534387756

Cover type:

Edition/Copyright: 04

Publisher: Brooks/Cole Publishing Co.

Published: 2004

International: No

Guided by problems that frequently arise in actual practice, James Higgins? book presents a wide array of nonparametric methods of data analysis that researchers will find useful. It discusses a variety of nonparametric methods and, wherever possible, stresses the connection between methods. For instance, rank tests are introduced as special cases of permutation tests applied to ranks. The author provides coverage of topics not often found in nonparametric textbooks, including procedures for multivariate data, multiple regression, multi-factor analysis of variance, survival data, and curve smoothing. This truly modern approach teaches non-majors how to analyze and interpret data with nonparametric procedures using today?s computing technology.

**Benefits: **

- The author provides a narrative presentation of very technical material, which helps non-statisticians understand difficult concepts.
- Many examples and exercises use real data.
- Selected computer output and code show how methods are implemented. Three statistical packages are featured: StatXact, Resampling Stats, and S-Plus.
- This book presents a comprehensive and accessible compilation of modern nonparametric techniques not often found in a single book, but rather in individual books on traditional nonparametric rank tests, permutations tests, bootstrapping procedures, nonparametric survival analysis, etc.
- Current references to the latest literature on each topic are included.

Table of Contents

1 ONE-SAMPLE METHODS

Preliminaries

A Nonparametric Test and Confidence Interval for the Median

Estimating the Population CDF and Quantiles

A Comparison of Statistical Tests

2 TWO-SAMPLE METHODS

A Two-Sample Permutation Test

Permutation Tests Based on the Median and Trimmed Means

Random Sampling the Permutations

Wilcoxon Rank-Sum Test

Wilcoxon Rank-Sum Test Adjusted for Ties

Mann-Whitney Test and a Confidence Interval

Scoring Systems

Test for Equality of Scale Parameters and an Omnibus Test

Selecting Among Two-Sample Tests

Large Sample Approximations

Exercises

3 K-SAMPLE METHODS

K-Sample Permutation Tests

The Kruskal-Wallis Test

Multiple Comparisons

Ordered Alternatives

Exercises

4 PAIRED COMPARISONS AND BLOCKED DESIGNS

Paired Comparison Permutation Test

Signed-Rank Test

Other Paired-Comparison Tests

A Permutation Test for a Randomized Complete Block Design

Friedman's Test for a Randomized Complete Block Design

Ordered Alternatives for a Randomized Complete Block Design

Exercises

5 TESTS FOR TRENDS AND ASSOCIATION

A Permutation Test for Correlation and Slope

Spearman Rank Correlation

Kendall's Tau

Permutation Tests for Contingency Tables

Fisher's Exact Test for a 2 X 2 Contingency Table

Contingency Tables With Ordered Categories

Mantel-Haenszel Test

Exercises

6 MULTIVARIATE TESTS

Two-Sample Multivariate Permutation Tests

Two-Sample Multivariate Rank Tests

Multivariate Paired Comparisons

Multivariate Rank Tests for Paired Comparisons

Multi-response Categorical Data

Exercises

7 ANALYSIS OF CENSORED DATA

Estimating the Survival Function

Permutation Tests for Two-Sample Censored Data

Gehan's Generalization of the Mann-Whitney-Wilcoxon Test

Scoring Systems for Censored Data

Tests Using Scoring Systems for Censored Data

Exercises

8 NONPARAMETRIC BOOTSTRAP METHODS

The Basic Bootstrap Method

Bootstrap Intervals for Location-Scale Models

BCA and Other Bootstrap Intervals

Correlation and Regression

Two-Sample Inference

Bootstrap Sampling from Several Populations

Bootstrap Sampling for Multiple Regression

Multivariate Bootstrap Sampling

Exercises

9 MULTIFACTOR EXPERIMENTS

Analysis of Variance Models

Aligned Rank Transform

Testing for Lattice-Ordered Alternatives

Exercises

10 SMOOTHING METHODS AND ROBUST MODEL FITTING

Estimating the Probability Density Function

Nonparametric Curve Smoothing

Robust and Rank-Based Regression

Exercises

TABLES

REFERENCES

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