Multivariate Exponential Families: A Concise Guide to Statistical Inference (Softcover)

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Gives a concise introduction to exponential familiesIncludes a variety of detailed examplesProvides a rigorous course for graduate students of statistics and mathematics as well as for researchers

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.

Stefan Bedbur has a postdoctoral position at the Chair in Statistics, RWTH Aachen University, where he was awarded the honorary title RWTH Lecturer in 2020. 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.

Stefan Bedbur has a postdoctoral position at the Chair in Statistics, RWTH Aachen University, where he was awarded the honorary title RWTH Lecturer in 2020.
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.