Resampling Methods : A Practical Guide to Data Analysis /
By: Good, Phillip I [author.].
Contributor(s): SpringerLink (Online service).
Material type:
BookPublisher: Boston, MA : Birkhäuser Boston, 2006.Edition: Third Edition.Description: XX, 218 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780817644444.Subject(s): Mathematics | Mathematical analysis | Analysis (Mathematics) | Probabilities | Statistics | Mathematics | Probability Theory and Stochastic Processes | Analysis | Statistical Theory and Methods | Statistics, general | Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences | Statistics for Life Sciences, Medicine, Health SciencesDDC classification: 519.2 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK7798 |
Software for Resampling -- Estimating Population Parameters -- Comparing Two Populations -- Choosing the Best Procedure -- Experimental Design and Analysis -- Categorical Data -- Multiple Variables and Multiple Hypotheses -- Model Building -- Decision Trees.
"…the author has packaged an excellent and modern set of topics around the development and use of quantitative models.... If you need to learn about resampling, this book would be a good place to start." —Technometrics (Review of the Second Edition) This thoroughly revised and expanded third edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. Only requiring minimal mathematics beyond algebra, the book provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware. Topics and Features * Practical presentation covers both the bootstrap and permutations along with the program code necessary to put them to work. * Includes a systematic guide to selecting the correct procedure for a particular application. * Detailed coverage of classification, estimation, experimental design, hypothesis testing, and modeling. * Suitable for both classroom use and individual self-study. New to the Third Edition * Procedures are grouped by application; a prefatory chapter guides readers to the appropriate reading matter. * Program listings and screen shots now accompany each resampling procedure: Whether one programs in C++, CART, Blossom, Box Sampler (an Excel add-in), EViews, MATLAB, R, Resampling Stats, SAS macros, S-PLUS, Stata, or StatXact, readers will find the program listings and screen shots needed to put each resampling procedure into practice. * To simplify programming, code for readers to download and apply is posted at http://www.springeronline.com/0-8176-4386-9. * Notation has been simplified and, where possible, eliminated. * A glossary and answers to selected exercises are included. With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of resampling methods. It is an essential resource for statisticians, biostatisticians, statistical consultants, students, and research professionals in the biological, physical, and social sciences, engineering, and technology.
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