The jackknife and the bootstrap are nonparametric methods for
assessing the errors in a statistical estimation problem. They
provide several advantages over the traditional parametric
approach: the methods are easy to describe and they apply to
arbitrarily complicated situations; distribution assumptions, such
as normality, are never made. This monograph connects the
jackknife, the bootstrap, and many other related ideas such as
cross-validation, random subsampling, and balanced repeated
replications into a unified exposition. The theoretical development
is at an easy mathematical level and is supplemented by a large
number of numerical examples. The methods described in this
monograph form a useful set of tools for the applied statistician.
They are particularly useful in problem areas where complicated
data structures are common, for example, in censoring, missing
data, and highly multivariate situations.
General
Imprint: |
Society For Industrial & Applied Mathematics,U.S.
|
Country of origin: |
United States |
Series: |
CBMS-NSF Regional Conference Series, v. 38 |
Release date: |
1982 |
Authors: |
Bradley Efron
|
Series editors: |
Ron Rozier
|
Dimensions: |
229 x 152 x 8mm (L x W x T) |
Format: |
Paperback - Trade
|
Pages: |
99 |
ISBN-13: |
978-0-89871-179-0 |
Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
|
LSN: |
0-89871-179-7 |
Barcode: |
9780898711790 |
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