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Statistical Modelling by Exponential Families
Contributor(s): Sundberg, Rolf (Author)

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 ISBN: 1108701116     ISBN-13: 9781108701112
Publisher: Cambridge University Press
OUR PRICE: $39.89  

Binding Type: Paperback - See All Available Formats & Editions
Published: August 2019
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Additional Information
BISAC Categories:
- Mathematics | Probability & Statistics - General
Dewey: 519.5
LCCN: 2019009281
Series: Institute of Mathematical Statistics Textbooks
Physical Information: 9" H x 0.5" W x 6" L (0.96 lbs) 296 pages
Features: Bibliography, Index, Price on Product
 
Descriptions, Reviews, Etc.
Publisher 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-L f 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.

Contributor Bio(s): Sundberg, Rolf: - 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.
 
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