Introduction to Modern Nonparametric Statistics

ISBN-10: 0534387756

ISBN-13: 9780534387754

Edition: 2004

Authors: James J. Higgins

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Description:

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.
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Book details

List price: $199.95
Copyright year: 2004
Publisher: Brooks/Cole
Publication date: 5/5/2003
Binding: Hardcover
Pages: 500
Size: 7.50" wide x 9.25" long x 0.75" tall
Weight: 2.398
Language: English

James J. Higgins is Professor of Statistics at Kansas State University and Fellow of the American Statistical Association. He is the co-author of the Duxbury textbook CONCEPTS IN PROBABILITY AND STOCHASTIC MODELING with Sallie Keller-McNulty and he is author of INTRODUCTION TO MODERN NONPARAMETRIC STATISTICS as well as having over 80 scientific publications to his credit. In addition, he is a statistical consultant for Kansas State Research and Extension. His research interests include nonparametric statistics and reliability theory.

One-Sample Methods
Preliminaries
A Nonparametric Test and Confidence Interval for the Median
Estimating the Population CDF and Quantiles
A Comparison of Statistical Tests
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
K-Sample Methods
K-Sample Permutation Tests
The Kruskal-Wallis Test
Multiple Comparisons
Ordered Alternatives
Exercises
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
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 ?e 2 Contingency Table
Contingency Tables With Ordered Categories
Mantel-Haenszel Test
Exercises
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
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
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
Multifactor Experiments
Analysis of Variance Models
Aligned Rank Transform
Testing for Lattice-Ordered Alternatives
Exercises
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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