Exponential Families in Theory and Practice, Paperback by Efron, Bradley, Lik...

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Item specifics

Condition
Like New: A book that looks new but has been read. Cover has no visible wear, and the dust jacket ...
Book Title
Exponential Families in Theory and Practice
ISBN
9781108715669
Subject Area
Mathematics
Publication Name
Exponential Families in Theory and Practice
Publisher
Cambridge University Press
Item Length
9 in
Subject
Probability & Statistics / General, General
Publication Year
2022
Series
Institute of Mathematical Statistics Textbooks Ser.
Type
Textbook
Format
Trade Paperback
Language
English
Item Height
0.6 in
Author
Bradley Efron
Item Width
6 in
Number of Pages
200 Pages

About this product

Product Identifiers

Publisher
Cambridge University Press
ISBN-10
1108715664
ISBN-13
9781108715669
eBay Product ID (ePID)
26057264217

Product Key Features

Number of Pages
200 Pages
Publication Name
Exponential Families in Theory and Practice
Language
English
Publication Year
2022
Subject
Probability & Statistics / General, General
Type
Textbook
Author
Bradley Efron
Subject Area
Mathematics
Series
Institute of Mathematical Statistics Textbooks Ser.
Format
Trade Paperback

Dimensions

Item Height
0.6 in
Item Length
9 in
Item Width
6 in

Additional Product Features

Intended Audience
College Audience
Dewey Edition
23
Reviews
'In this book, Brad Efron illuminates the exponential family as a practical, extendible, and crucial ingredient in all manners of data analysis, be they Bayesian, frequentist, or machine learning. He shows us how to shape, understand, and employ these distributions in both algorithms and analysis. The book is crisp, insightful, and indispensable.' David Blei, Columbia University, 'Exponential families can be magical in simplifying both theoretical and applied statistical analyses. Brad Efron's wonderful book exposes their secrets, from R. A. Fisher's early magic to Efron's own bootstrap: an essential text for understanding how data of all sizes can be approached scientifically.' Stephen, 'This book provides an original and accessible study of statistical inference in the class of models called exponential families. The mathematical properties and flexibility of this class makes the models very useful for statistical practice - they underpin the class of generalized linear models, for example. Writing with his characteristic elegance and clarity, Efron shows how exponential families underpin, and provide insight into, many modern topics in statistical science, including bootstrap inference, empirical Bayes methodology, high-dimensional inference, analysis of survival data, missing data, and more.' Nancy Reid, University of Toronto, 'This book provides a unique perspective on exponential families, bringing together theory and methods into a unified whole. No other text covers the range of topics in this text. If you want to understand the 'why' as well as the 'how' of exponential families, then this book should be on your bookshelf.' Larry Wasserman, Carnegie Mellon University, 'I am excited to see the publication of this monograph on exponential families by my friend and colleague Brad Efron. I learned some of this material during my Ph.D. studies at Stanford from the maestro himself, as well as the geometry of curved exponential families, Hoeffding's lemma, the Lindsey method, and the list goes on. They have lived with me my entire career and informed our work on GAMs and sparse GLMs. Generations of Stanford students have shared this privilege, and now generations in the future will be able to enjoy the unique Efron style.' Trevor Hastie, Stanford University
Illustrated
Yes
Dewey Decimal
519.5
Table Of Content
1. One-parameter exponential families; 2. Multiparameter exponential families; 3. Generalized linear models; 4. Curved exponential families, eb, missing data, and the em algorithm; 5. Bootstrap confidence intervals; Bibliography; Index.
Synopsis
During the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master's students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn't require advanced mathematical preparation., This book is aimed at Ph.D. and advanced M.S. students of statistics who need to understand modern statistical theory both in its exponential family structure and its applications, without requiring advanced mathematical preparation. Connections with logistic regression, survival analysis, Bayesian methods, and false discovery rates are emphasized.
LC Classification Number
QA276.7

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