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Statistical Modelling by Exponential Families on Hardback by Rolf Sundberg
Statistical Modelling by Exponential Families on Hardback by Rolf Sundberg
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Statistical Modelling by Exponential Families

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Description

This book is a readable, digestible introduction to exponential families, encompassing statistical models based on the most useful distributions in statistical theory, including the normal, gamma, binomial, Poisson, and negative binomial. Strongly motivated by applications, it presents the essential theory and then demonstrates the theory's practical potential by connecting it with developments in areas like item response analysis, social network models, conditional independence and latent variable structures, and point process models. Extensions to incomplete data models and generalized linear models are also included. In addition, the author gives a concise account of the philosophy of Per Martin-Löf in order to connect statistical modelling with ideas in statistical physics, including Boltzmann's law. Written for graduate students and researchers with a background in basic statistical inference, the book includes a vast set of examples demonstrating models for applications and exercises embedded within the text as well as at the ends of chapters.

Author Biography:

Rolf Sundberg is Professor Emeritus of Statistical Science at Stockholms Universitet. His work embraces both theoretical and applied statistics, including principles of statistics, exponential families, regression, chemometrics, stereology, survey sampling inference, molecular biology, and paleoclimatology. In 2003, with M. Linder, he won the award for best theoretical paper in the Journal of Chemometrics for their work on multivariate calibration, and in 2017 he was named Statistician of the Year by the Swedish Statistical Society.
Release date NZ
August 29th, 2019
Author
Audience
  • Postgraduate, Research & Scholarly
Illustrations
Worked examples or Exercises; 22 Line drawings, black and white
Pages
296
Dimensions
157x235x20
ISBN-13
9781108476591
Product ID
29775198

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