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Series Approximation Methods in Statistics

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This book is intended primarily for advanced graduate students and researchers in the field needing a collection of core results in a uniform notation, with bibliographical references to further examples and applications. It assumes familiarity with real and univariate complex analysis, and vector calculus.

Author(s): John E. Kolassa (auth.)
Series: Lecture Notes in Statistics 88
Edition: 3rd ed
Publisher: Springer New York
Year: 1994

Language: English
Pages: 228
City: New York
Tags: Probability Theory and Stochastic Processes

Front Matter....Pages N2-viii
Asymptotics in General....Pages 1-4
Characteristic Functions and the Berry-Esseen Theorem....Pages 5-20
Edgeworth Series....Pages 21-48
Saddlepoint Series for Densities....Pages 49-69
Saddlepoint Series for Distribution Functions....Pages 70-80
Multivariate Expansions....Pages 81-94
Conditional Distribution Approximations....Pages 95-111
Applications to Likelihood Ratio and Maximum Likelihood Statistics....Pages 112-131
Other Topics....Pages 132-137
Computational Aids....Pages 138-142
Back Matter....Pages 143-153