An Introduction to the Bootstrap

Bradley Efron, R.J. Tibshirani, R.J. Tibshirani (Stanford university Stanford University Stanford University, California, USA Stanford University, California, USA) , N. Reid (University of Toronto, Ontario, Canada)

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English

Chapman & Hall/CRC
15 May 1994

Probability & statistics; Applied mathematics

Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.

By: Bradley Efron, R.J. Tibshirani, R.J. Tibshirani (Stanford university Stanford University Stanford University, California, USA Stanford University, California, USA)
Series edited by: N. Reid (University of Toronto Ontario Canada), Valerie Isham
Imprint: Chapman & Hall/CRC
Country of Publication: United Kingdom
Volume: 57
Dimensions: Height: 229mm, Width: 152mm, Spine: 28mm
Weight: 756g
ISBN: 9780412042317
ISBN 10: 0412042312
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Pages: 456
Publication Date: 15 May 1994
Audience: College/higher education , Professional and scholarly , Primary , Undergraduate
Format: Hardback
Publisher's Status: Active

Bradley Efron, Department of Statistics Stanford University and Robert J. Tibshirani, Department of Preventative Medicine and Biostatistics and Department of Statistics, University of Toronto.

Reviews for An Introduction to the Bootstrap

""...an excellent book, and worth a reading by most students and practitioners in statistics... Throughout the book, the authors have spent a lot of effort in introducing difficult ideas in a simple, easy-to-understand manner..."" - Hong Kong Statistical Society Newsletter ""... written in a style that makes difficult statistical concepts easy to understand ...a wonderful text for the engineer who would like to apply and understand the many different bootstrap techniques that have appeared in the literature in the last fifteen years. It makes an excellent reference text that should grace the shelves of both statisticians and non-statisticians."" - Journal of Quality Technology