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Multivariate Exponential Families: A Concise Guide to Statistical Inference

Multivariate Exponential Families: A Concise Guide to Statistical Inference
Catalogue Information
Field name Details
Dewey Class 519.5
Title Multivariate Exponential Families: A Concise Guide to Statistical Inference ([EBook] /) / by Stefan Bedbur, Udo Kamps.
Author Bedbur, Stefan
Added Personal Name Kamps, Udo
Other name(s) SpringerLink (Online service)
Edition statement 1st ed. 2021.
Publication Cham : : Springer International Publishing : : Imprint: Springer, , 2021.
Physical Details X, 142 p. 1 illus. : online resource.
Series SpringerBriefs in Statistics 2191-5458
ISBN 9783030819002
Summary Note 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.:
Contents note Introduction -- Parametrizations and Basic Properties -- Distributional and Statistical Properties -- Parameter Estimation -- Hypotheses Testing -- Exemplary Multivariate Applications.
Mode of acces to digital resource Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format.
System details note Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site https://doi.org/10.1007/978-3-030-81900-2
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