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

Phillip Good

ISBN 10: 1439855501 / ISBN 13: 9781439855508

Published by Chapman And Hall/CRC, 2011

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Resampling methods techniques for repeatedly resampling data to obtain results are being used in virtually every research area. This practical guide discusses the applications of these methods, especially in the areas of microarrays and data mining. Each chapter contains a wealth of exercises along with R and Stata code. Written by a leading authority in the field, the text covers such topics as estimation, the bootstrap method, multivariate tests, decision trees, categorical data, multiple hypotheses, and model building.

Seller Inventory # 9781439855508

Bibliographic Details

• Title: A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling
• Publisher: Chapman And Hall/CRC
• Publication Date: 2011
• Language: English
• ISBN 10: 1439855501
• ISBN 13: 9781439855508
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 17D millimeters 240 millimeters width by 161 millimeters height by 17 millimeters depth
• Weight: 508 grams

About this title

Synopsis

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.

About 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).