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Author: Rolf Sundberg
Publisher: Cambridge University Press
Published: 2019-08-29
Total Pages: 297
ISBN-13: 1108476597
DOWNLOAD EBOOKA readable, digestible introduction to essential theory and wealth of applications, with a vast set of examples and numerous exercises.
Author: Martin J. Wainwright
Publisher: Now Publishers Inc
Published: 2008
Total Pages: 324
ISBN-13: 1601981848
DOWNLOAD EBOOKThe core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.
Author: Lawrence D. Brown
Publisher: IMS
Published: 1986
Total Pages: 302
ISBN-13: 9780940600102
DOWNLOAD EBOOKAuthor: Rolf Sundberg
Publisher: Cambridge University Press
Published: 2019-08-29
Total Pages: 297
ISBN-13: 1108759912
DOWNLOAD EBOOKThis 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: Bo-Cheng Wei
Publisher:
Published: 1998-09
Total Pages: 248
ISBN-13:
DOWNLOAD EBOOKThis book gives a comprehensive introduction to exponential family nonlinear models, which are the natural extension of generalized linear models and normal nonlinear regression models. The differential geometric framework is presented for these models and geometric methods are widely used in this book. This book is ideally suited for researchers in statistical interfaces and graduate students with a basic knowledge of statistics.
Author: Seth Sullivant
Publisher: American Mathematical Soc.
Published: 2018-11-19
Total Pages: 506
ISBN-13: 1470435179
DOWNLOAD EBOOKAlgebraic statistics uses tools from algebraic geometry, commutative algebra, combinatorics, and their computational sides to address problems in statistics and its applications. The starting point for this connection is the observation that many statistical models are semialgebraic sets. The algebra/statistics connection is now over twenty years old, and this book presents the first broad introductory treatment of the subject. Along with background material in probability, algebra, and statistics, this book covers a range of topics in algebraic statistics including algebraic exponential families, likelihood inference, Fisher's exact test, bounds on entries of contingency tables, design of experiments, identifiability of hidden variable models, phylogenetic models, and model selection. With numerous examples, references, and over 150 exercises, this book is suitable for both classroom use and independent study.
Author: Murray A. Aitkin
Publisher: Oxford University Press
Published: 1989
Total Pages: 390
ISBN-13: 9780198522034
DOWNLOAD EBOOKThe analysis of data by statistical modelling is becoming increasingly important. This book presents both the theory of statistical modelling with generalized linear models and the application of the theory to practical problems using the widely available package GLIM. The authors have takenpains to integrate the theory with many practical examples which illustrate the value of interactive statistical modelling. Throughout the book theoretical issues of formulating and simplifying models are discussed, as are problems of validating the models by the detection of outliers and influential observations. The book arises from short courses given at the University of Lancaster's Centre for Applied Statistics, with an emphasis on practical programming in GLIM and numerous examples. A wide range of case studies is provided, using the normal, binomial, Poisson, multinomial, gamma, exponential andWeibull distributions. A feature of the book is a detailed discussion of survival analysis. Statisticians working in a wide range of fields, including biomedical and social sciences, will find this book an invaluable desktop companion to aid their statistical modelling. It will also provide a text for students meeting the ideas of statistical modelling for the first time.
Author: Bent Jorgensen
Publisher: CRC Press
Published: 1997-06-01
Total Pages: 264
ISBN-13: 9780412997112
DOWNLOAD EBOOKThe theory of dispersion models straddles both statistics and probability, and involves an encyclopedic collection of tools, such as exponential families, asymptotic theory, stochastic processes, Tauber theory, infinite divisibility, and stable distributions. The Theory of Dispersion Models introduces the reader to these models, which serve as error distributions for generalized linear models, and looks at their applications within this context.
Author: Annette J. Dobson
Publisher: Springer
Published: 2013-11-11
Total Pages: 133
ISBN-13: 1489931740
DOWNLOAD EBOOKThis book is about generalized linear models as described by NeIder and Wedderburn (1972). This approach provides a unified theoretical and computational framework for the most commonly used statistical methods: regression, analysis of variance and covariance, logistic regression, log-linear models for contingency tables and several more specialized techniques. More advanced expositions of the subject are given by McCullagh and NeIder (1983) and Andersen (1980). The emphasis is on the use of statistical models to investigate substantive questions rather than to produce mathematical descriptions of the data. Therefore parameter estimation and hypothesis testing are stressed. I have assumed that the reader is familiar with the most commonly used statistical concepts and methods and has some basic knowledge of calculus and matrix algebra. Short numerical examples are used to illustrate the main points. In writing this book I have been helped greatly by the comments and criticism of my students and colleagues, especially Anne Young. However, the choice of material, and the obscurities and errors are my responsibility and I apologize to the reader for any irritation caused by them. For typing the manuscript under difficult conditions I am grateful to Anne McKim, Jan Garnsey, Cath Claydon and Julie Latimer.
Author: Jean-Francois Dupuy
Publisher: Elsevier
Published: 2018-11-19
Total Pages: 194
ISBN-13: 008102374X
DOWNLOAD EBOOKStatistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementation. All methods are illustrated on datasets arising in the field of health economics. As several tools have been developed to tackle over-dispersed and zero-inflated data (such as adjustment methods and zero-inflated models), this book covers the topic in a comprehensive and interesting manner. - Includes reading on several levels, including methodology and applications - Presents the state-of-the-art on the most recent zero-inflated regression models - Contains a single dataset that is used as a common thread for illustrating all methodologies - Includes R code that allows the reader to apply methodologies
