A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling

Overview

Distribution-free resampling methods—permutation tests, decision trees, and the bootstrap—are used today in virtually every research area. A Practitioner’s Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression ...

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Overview

Distribution-free resampling methods—permutation tests, decision trees, and the bootstrap—are used today in virtually every research area. A Practitioner’s Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression methods.

Highlights

  • Each chapter contains dozens of thought provoking questions, along with applicable R and Stata code
  • Methods are illustrated with examples from agriculture, audits, bird migration, clinical trials, epidemiology, image processing, immunology, medicine, microarrays and gene selection
  • Lists of commercially available software for the bootstrap, decision trees, and permutation tests are incorporated in the text
  • Access to APL, MATLAB, and SC code for many of the routines is provided on the author’s website
  • The text covers estimation, two-sample and k-sample univariate, and multivariate comparisons of means and variances, sample size determination, categorical data, multiple hypotheses, and model building

Statistics practitioners will find the methods described in the text easy to learn and to apply in a broad range of subject areas from A for Accounting, Agriculture, Anthropology, Aquatic science, Archaeology, Astronomy, and Atmospheric science to V for Virology and Vocational Guidance, and Z for Zoology.

Practitioners and research workers and in the biomedical, engineering and social sciences, as well as advanced students in biology, business, dentistry, medicine, psychology, public health, sociology, and statistics will find an easily-grasped guide to estimation, testing hypotheses and model building.

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Editorial Reviews

From the Publisher
"This is an elementary introduction to the use of resampling methods, such as permutation tests and bootstrap methods, applied in a wide variety of statistical problems. … The book describes sources for code to run the methods it describes, in a variety of languages, and also illustrates using R and Stata code segments. … There are many exercises."
—David J. Hand, International Statistical Review (2013), 81, 2
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Product Details

  • ISBN-13: 9781439855508
  • Publisher: Taylor & Francis
  • Publication date: 9/15/2011
  • Pages: 224
  • Product dimensions: 6.10 (w) x 9.30 (h) x 0.90 (d)

Meet the Author

Phillip Good is the author of 18 novels, 625 popular articles in magazines and newspapers, scholarly articles in the fields of astrophysics, biology, biostatistics, computer science, probability, and statistics, and nine statistical texts including Applying Statistics in the Courtroom: A New Approach for Attorneys and Expert Witnesses, Chapman Hall, London, 2001. ISBN 1-58488-271-9, and Managers' Guide to the Design and Conduct of Clinical Trials, Wiley, NY, 2002 (2nd edition, 2006).

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Table of Contents

Wide Range of Applications
The Resampling Methods
Fields of Application

Estimation and the Bootstrap

Precision of an Estimate
Confidence Intervals
Improved Confidence Intervals
Estimating Bias
Determining Sample Size

Software for Use with the Bootstrap and Permutation Tests

AFNI
Blossom Statistical Analysis Package
Eviews
HaploView
MatLab®
NCSS
PAUP
R.
SAS
S-Plus
SPSS Exact Tests
Stata
Statistical Calculator
StatXact
Testimate

Comparing Two Populations

A Distribution-Free Test
Some Statistical Considerations
Computing the p-Value
Other Two-Sample Comparisons
Two-Sided Test
Rank Tests
Matched Pairs
R Code
Stata
Test for Nonequivalence
Underlying Assumptions
Comparing Variances

Multiple Variables

Single-Valued Test Statistic
Combining Univariate Tests

Experimental Design and Analysis
Separating Signal from Noise
k-Sample Comparison
Multiple Factors
Eliminating the Effects of Multiple Covariates
Crossover Designs
Which Sets of Labels Should We Rearrange?
Determining Sample Size
Missing Combinations

Categorical Data

Fisher’s Exact Test.
Odds Ratio.4
Unordered r × c Contingency Tables
Ordered Statistical Tables
Multidimensional Arrays

Multiple Hypotheses

Controlling the Family-Wise Error Rate
Controlling the False Discovery Rate
Software for Performing Multiple Simultaneous Tests
Testing for Trend

Model Building

Regression Models
Applying the Permutation Test
Applying the Bootstrap
Prediction Error
Validation

Classification

Cluster Analysis
Classification
Decision Trees
Decision Trees vs. Regression
Which Decision Tree Algorithm Is Best for Your Application?
Reducing the Rate of Misclassification
Comparison of Classification Tree Algorithms
Validation vs. Cross-Validation

Restricted Permutations

Quasi Independence
Complete Factorials
Synchronized Permutations
Model Validation

References


Appendix A: Basic Concepts in Statistics

Additive vs. Multiplicative Models
Central Values
Combinations and Rearrangements
Dispersion
Frequency Distribution and Percentiles
Linear vs. Nonlinear Regression
Regression Methods

Appendix B: Proof of Theorems

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