Publication Date

1999

Document Type

Dissertation/Thesis

First Advisor

Polansky, Alan M.

Degree Name

M.S. (Master of Science)

Department

Department of Mathematical Sciences

LCSH

Bootstrap (Statistics)||Confidence intervals||Statistical tolerance regions||Nonparametric statistics

Abstract

Confidence sets are constructed in almost any statistical analysis. Both parametric and nonparametric approaches can be taken to construct a confidence set for a parameter [theta]. In the one dimensional case, a disjoint interval is essentially the only form available for a confidence set, and construction of such an interval has been extensively studied. For the multiparameter case, the problem is not that simple, as the question of shape of the set arises and very few alternatives to the normal approximation method are available. One alternative is to use bootstrap theory to construct such confidence sets for parameter vector. These methods are yet to be studied empirically for comparison purposes in terms of coverage accuracy. This thesis represents an effort to focus on the empirical performance of some of the bootstrap methods available for construction of confidence sets for a vector parameter.

Comments

Includes bibliographical references (pages [15]-16).

Extent

v, 29 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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