Authors:
- Gives a concise introduction to exponential families
- Includes a variety of detailed examples
- Provides a rigorous course for graduate students of statistics and mathematics as well as for researchers
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
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Table of contents (6 chapters)
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Front Matter
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Back Matter
About this book
This book provides a concise introduction to exponential families. Parametric families of probability distributions and their properties are extensively studied in the literature on statistical modeling and inference. Exponential families of distributions comprise density functions of a particular form, which enables general assertions and leads to nice features. With a focus on parameter estimation and hypotheses testing, the text introduces the reader to distributional and statistical properties of multivariate and multiparameter exponential families along with a variety of detailed examples. The material is widely self-contained and written in a mathematical setting. It may serve both as a concise, mathematically rigorous course on exponential families in a systematic structure and as an introduction to Mathematical Statistics restricted to the use of exponential families.
Authors and Affiliations
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Institute of Statistics, RWTH Aachen University, Aachen, Germany
Stefan Bedbur, Udo Kamps
About the authors
Udo Kamps holds the Chair in Statistics at the Institute of Statistics, RWTH Aachen University, and is an elected member of the International Statistical Institute since 2000.
Their main fields of work are Applied and Mathematical Statistics, Reliability Theory, and Models for Ordered Data.
Bibliographic Information
Book Title: Multivariate Exponential Families: A Concise Guide to Statistical Inference
Authors: Stefan Bedbur, Udo Kamps
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-030-81900-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Softcover ISBN: 978-3-030-81899-9Published: 08 October 2021
eBook ISBN: 978-3-030-81900-2Published: 07 October 2021
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: X, 142
Number of Illustrations: 1 b/w illustrations
Topics: Statistical Theory and Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics for Life Sciences, Medicine, Health Sciences, Probability and Statistics in Computer Science