Pub. Date:
Springer-Verlag New York, LLC
Series Approximation Methods in Statistics / Edition 2

Series Approximation Methods in Statistics / Edition 2

by John E. Kolassa


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Asymptotic techniques have long been important in statistical inference; these techniques remain important in the age of fast computing because some exact answers are still either conceptually unavailable or practically out of reach. This book presents theoretical results relevant to Edgeworth and saddlepoint expansions to densities and distribution functions. It provides examples of their application in some simple, and in a few complicated, settings. Numerical and asymptotic assessments of accuracy are presented. Variants of these expansions, including much of modern likelihood theory, are discussed. Applications to lattice distributions are extensively treated.

Product Details

ISBN-13: 9780387982243
Publisher: Springer-Verlag New York, LLC
Publication date: 01/28/1997
Series: Lecture Notes in Statistics
Edition description: 2ND
Pages: 204
Product dimensions: 6.14(w) x 9.21(h) x 0.43(d)

About the Author

John E. Kolassa is Assistant Professor of Biostatistics at the University of Rochester.

Table of Contents

1 Asymptotics in General 1

2 Characteristic Functions and the Berry-Esseen Theorem 7

3 Edgeworth Series 31

4 Saddlepoint Series for Densities 63

5 Saddlepoint Series for Distribution Functions 89

6 Multivariate Expansions 109

7 Conditional Distribution Approximations 139

8 Applications to Wald, Likelihood Ratio, and Maximum Likelihood Statistics 165

9 Other Topics 189

10 Computational Aids 199

Bibliography 205

Author Index 215

Subject Index 217

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