A practitioner's guide to resampling for data analysis, data mining, and modeling - Good, Phillip I. (Boca Raton, FL CRC Press, 2012)
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.
1. Wide Range of Applications: Resampling Methods ; Fields of Application -- 2. Estimation and the Bootstrap: Precision of an Estimate ; Confidence Intervals ; Improved Confidence Intervals ; Estimating Bias ; Determining Sample Size -- 3. 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 -- 4. 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 -- 5. Multiple Variables: Single-Valued Test Statistic ; Combining Univariate Tests -- 6. 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 -- 7. Categorical Data: Fisher's Exact Test. ; Odds Ratio.4 ; Unordered r x c Contingency Tables ; Ordered Statistical Tables ; Multidimensional Arrays -- 8. Multiple Hypotheses: Controlling the Family-Wise Error Rate ; Controlling the False Discovery Rate ; Software for Performing Multiple Simultaneous Tests ; Testing for Trend -- 9. Model Building: Regression Models ; Applying the Permutation Test ; Applying the Bootstrap ; Prediction Error ; Validation -- 10. 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 --11. Restricted Permutations: Quasi Independence ; Complete Factorials ; Synchronized Permutations ; Model Validation -- 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.
Includes bibliographical references and index.
Keywords: Resampling (Statistics)