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Bootstrapping, a computational nonparametric technique for "re-sampling," enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percenti
- Sales Rank: #1243352 in Books
- Published on: 1993-08-09
- Original language: English
- Number of items: 1
- Dimensions: 8.50" h x .17" w x 5.51" l, .21 pounds
- Binding: Paperback
- 80 pages
About the Author
Christopher Z. Mooney is a professor of political studies with a joint appointment in the Institute of Government and Public Affairs. Mooney studies U.S. state politics and policy, with special focus on legislative decision making, morality policy, and legislative term limits. He is the founding editor of State Politics and Policy Quarterly, the premier academic journal in its field and has published dozens of articles and books, including Lobbying Illinois - How You Can Make a Difference in Public Policy. Prior to arriving at UIS in 1999, he taught at West Virginia University and the University of Essex in the United Kingdom
Most helpful customer reviews
0 of 0 people found the following review helpful.
I found errors that made me distrust the book
By A reader
I borrowed the book from our library to decide whether or not to buy the book. Unfortunately I very soon found errors. The first one in the Jackknife section. All the so-called pseudo values in Table 1.4 are wrong. The authors point out that "the pseudo values are quite close to the values of the original data". That should have made the authors realize that the pseudo values are wrong. For the sample mean, the pseudo values are *exactly* equal to the original data (as can be seen from Equation (5) in the booklet). If the authors did not know this basic property of the jackknife, it is hard to trust anything else in the book. Needless to say, I won't be buying this book.
35 of 39 people found the following review helpful.
simple introduction to bootstrap
By Michael R. Chernick
This monograph is typical of the short monographs in the SAGE series. It provides a gentle (light on mathematics) introduction to the topic with social scientists in mind. Some applications are presented and it provides a good list of references. It was written prior to the now popular book by Efron and Tibshirani and others that followed. At times it is overly simplistic in order to provide concepts without much mathematics. However, most of the descriptions are accurate. It provides an intuitive understanding of bootstrap, presents some applications, exposes the wide variety of potential applications, puts things in an appropriate historical prospective and briefly describes situations where the bootstrap fails. My book "Bootstrap Methods" published by Wiley in 1999 has many of the same objectives and features of this monograph. For a thorough and more recent account of the literature and more detailed examples see my book. Other books including Efron and Tibshirani (1993), Davison and Hinkley (1997), Hall (1992), Shao and Tu (1995) and Manly (1997) provide more detail, theory and mathematics. This book is good though, as a place to get that first quick and gentle introduction to the subject.
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