A practitioner's guide to resampling for data analysis, data mining, and modeling
- Good, Phillip I.
- Boca Raton, Fla. : CRC Press, 2012.
- Physical description
- x, 214 p. : ill. ; 24 cm.
QA278.8 .G64 2012
- Unknown QA278.8 .G64 2012
- Includes bibliographical references (p. 193-209) and index.
- 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(R) 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 x 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.
- (source: Nielsen Book Data)
- Publisher's Summary
- 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.
(source: Nielsen Book Data)
- Publication date
- Phillip I. Good.