A practitioner's guide to resampling for data analysis, data mining, and modeling
著者
書誌事項
A practitioner's guide to resampling for data analysis, data mining, and modeling
（A Chapman & Hall book）
CRC Press, c2012
大学図書館所蔵 件 / 全6件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
Includes bibliographical references (p. 193-209) and index
内容説明・目次
内容説明
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.
目次
Wide Range of Applications. Estimation and the Bootstrap. Software for Use with the Bootstrap and Permutation Tests. Comparing Two Populations. Multiple Variables. Experimental Design and Analysis. Categorical Data. Multiple Hypotheses. Model Building. Classification. Restricted Permutations. References. Appendix A: Basic Concepts in Statistics. Appendix B: Proof of Theorems. Author Index. Subject Index.
「Nielsen BookData」 より