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

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.

《無分配重抽樣方法——排列檢定、決策樹和自助法》是如今在幾乎所有研究領域中都使用的工具。本書詳細介紹了如何使用自助法來估計基於樣本的估計值的精確度，確定樣本大小，使用數據排列來檢驗假設，以及使用易於解釋的決策樹來替代晦澀難懂的回歸方法。

重點如下：
- 每章都包含許多發人深省的問題，以及適用於R和Stata的代碼示例。
- 以農業、審計、鳥類遷徙、臨床試驗、流行病學、圖像處理、免疫學、醫學、微陣列和基因選擇等領域的實例來說明方法。
- 書中還列出了商業上可用的自助法、決策樹和排列檢定軟件。
- 作者的網站提供了APL、MATLAB和SC代碼的許多例程。
- 本書涵蓋了估計、兩樣本和k樣本單變量和多變量均值和變異數比較、樣本大小確定、分類數據、多重假設和模型構建等內容。

統計從業人員會發現本書中描述的方法易於學習和應用於各個學科領域，從會計、農業、人類學、水生科學、考古學、天文學和大氣科學到病毒學和職業指導，再到動物學。

生物醫學、工程和社會科學的從業人員和研究人員，以及生物學、商業、牙科、醫學、心理學、公共衛生學、社會學和統計學的高級學生，都能在本書中找到易於理解的估計、假設檢驗和模型構建指南。