Joseph M. Hilbe's Books

Bayesian Models for Astrophysical Data

Using R, JAGS, Python, and Stan

By: Joseph M. Hilbe,Rafael S. de Souza,Emille E. O. Ishida

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Bayesian Models for Astrophysical Data

Using R, JAGS, Python, and Stan

By: Joseph M. Hilbe,Rafael S. de Souza,Emille E. O. Ishida

This comprehensive guide to Bayesian methods in astronomy enables hands-on work by supplying complete R, JAGS, Python, and Stan code, to use directly or to adapt. It begins by examining the normal model from both frequentist and Bayesian perspectives and then progresses to a full range of Bayesian generalized linear and mixed or hierarchical models, as well as additional types of models such as ABC and INLA. The book provides code that is largely unavailable elsewhere and includes details on interpreting and evaluating Bayesian models. Initial discussions offer models in synthetic form so that readers can easily adapt them to their own data; later the models are applied to real astronomical data. The consistent focus is on hands-on modeling, analysis of data, and interpretations that address scientific questions. A must-have for astronomers, its concrete approach will also be attractive to researchers in the sciences more generally.

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Page Count

429

Publisher

Cambridge University Press

Published Date

ISBN 10

1108210740

ISBN 13

9781108210744

Methods of Statistical Model Estimation

By: Joseph M. Hilbe,Andrew P. Robinson

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Methods of Statistical Model Estimation

By: Joseph M. Hilbe,Andrew P. Robinson

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content. Therefore, code is written for clarity of exposition rather than stability or speed of execution. Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them. See Professor Hilbe discuss the book.

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Page Count

257

Publisher

CRC Press

Published Date

ISBN 10

1439858020

ISBN 13

9781439858028

R for Stata Users

By: Robert A. Muenchen,Joseph M. Hilbe

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R for Stata Users

By: Robert A. Muenchen,Joseph M. Hilbe

Stata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download.

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549

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1441913181

ISBN 13

9781441913180

Generalized Linear Models and Extensions, Second Edition

By: James W. Hardin,James William Hardin,Joseph M. Hilbe,Joseph Hilbe

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Generalized Linear Models and Extensions, Second Edition

By: James W. Hardin,James William Hardin,Joseph M. Hilbe,Joseph Hilbe

Deftly balancing theory and application, this book stands out in its coverage of the derivation of the GLM families and their foremost links. This edition has new sections on discrete response models, including zero-truncated, zero-inflated, censored, and hurdle count models, as well as heterogeneous negative binomial, and more.

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413

Publisher

Stata Press

Published Date

ISBN 10

1597180149

ISBN 13

9781597180146

Negative Binomial Regression

By: Joseph M. Hilbe

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Negative Binomial Regression

By: Joseph M. Hilbe

This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation and evaluation. Complete Stata and R codes are provided throughout the text, with additional code (plus SAS), derivations and data provided on the book's website. Written for the practising researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data.

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573

Publisher

Cambridge University Press

Published Date

ISBN 10

1139500066

ISBN 13

9781139500067

Logistic Regression Models

By: Joseph M. Hilbe

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Logistic Regression Models

By: Joseph M. Hilbe

Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The text illustrates how to apply the various models t

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Page Count

658

Publisher

CRC Press

Published Date

ISBN 10

1420075772

ISBN 13

9781420075779

Generalized Estimating Equations, Second Edition

By: James W. Hardin,Joseph M. Hilbe

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Generalized Estimating Equations, Second Edition

By: James W. Hardin,Joseph M. Hilbe

Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in the final chapter as well as on the book’s website. This second edition incorporates comments and suggestions from a variety of sources, including the Statistics.com course on longitudinal and panel models taught by the authors. Other enhancements include an examination of GEE marginal effects; a more thorough presentation of hypothesis testing and diagnostics, covering competing hierarchical models; and a more detailed examination of previously discussed subjects. Along with doubling the number of end-of-chapter exercises, this edition expands discussion of various models associated with GEE, such as penalized GEE, cumulative and multinomial GEE, survey GEE, and quasi-least squares regression. It also offers a thoroughly new presentation of model selection procedures, including the introduction of an extension to the QIC measure that is applicable for choosing among working correlation structures. See Professor Hilbe discuss the book.

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Page Count

280

Publisher

CRC Press

Published Date

ISBN 10

1439881138

ISBN 13

9781439881132

Modeling Count Data

By: Joseph M. Hilbe

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Modeling Count Data

By: Joseph M. Hilbe

This book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models.

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301

Publisher

Cambridge University Press

Published Date

ISBN 10

1107028337

ISBN 13

9781107028333

Quasi-Least Squares Regression

By: Justine Shults,Joseph M. Hilbe

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Quasi-Least Squares Regression

By: Justine Shults,Joseph M. Hilbe

Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a d

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Page Count

221

Publisher

CRC Press

Published Date

ISBN 10

1420099949

ISBN 13

9781420099942

Practical Guide to Logistic Regression

By: Joseph M. Hilbe

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Practical Guide to Logistic Regression

By: Joseph M. Hilbe

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fishe

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Page Count

170

Publisher

CRC Press

Published Date

ISBN 10

1498709583

ISBN 13

9781498709583

Generalized Estimating Equations

By: James W. Hardin,Joseph M. Hilbe

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Generalized Estimating Equations

By: James W. Hardin,Joseph M. Hilbe

Although powerful and flexible, the method of generalized linear models (GLM) is limited in its ability to accurately deal with longitudinal and clustered data. Developed specifically to accommodate these data types, the method of Generalized Estimating Equations (GEE) extends the GLM algorithm to accommodate the correlated data encountered in heal

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Page Count

237

Publisher

CRC Press

Published Date

ISBN 10

1420035282

ISBN 13

9781420035285

Negative Binomial Regression

By: Joseph M. Hilbe

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Negative Binomial Regression

By: Joseph M. Hilbe

A substantial enhancement of the only text devoted entirely to the negative binomial model and its many variations.

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573

Publisher

Cambridge University Press

Published Date

ISBN 10

0521198151

ISBN 13

9780521198158

Practical Guide to Logistic Regression

By: Joseph M. Hilbe

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Practical Guide to Logistic Regression

By: Joseph M. Hilbe

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170

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CRC Press

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ISBN 10

1498709583

ISBN 13

9781498709583

Bayesian Models for Astrophysical Data

Using R, JAGS, Python, and Stan

By: Joseph M. Hilbe,Rafael S. de Souza,Emille E. O. Ishida

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Bayesian Models for Astrophysical Data

Using R, JAGS, Python, and Stan

By: Joseph M. Hilbe,Rafael S. de Souza,Emille E. O. Ishida

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Solve jigsaw puzzle

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Page Count

429

Publisher

Cambridge University Press

Published Date

ISBN 10

1108210740

ISBN 13

9781108210744

Book's by Joseph M. Hilbe

Bayesian Models for Astrophysical Data

Bayesian Models for Astrophysical Data

Methods of Statistical Model Estimation

Methods of Statistical Model Estimation

R for Stata Users

R for Stata Users

Generalized Linear Models and Extensions, Second Edition

Generalized Linear Models and Extensions, Second Edition

Negative Binomial Regression

Negative Binomial Regression

Logistic Regression Models

Logistic Regression Models

Generalized Estimating Equations, Second Edition

Generalized Estimating Equations, Second Edition

Modeling Count Data

Modeling Count Data

Quasi-Least Squares Regression

Quasi-Least Squares Regression

Practical Guide to Logistic Regression

Practical Guide to Logistic Regression

Generalized Estimating Equations

Generalized Estimating Equations

Negative Binomial Regression

Negative Binomial Regression

Practical Guide to Logistic Regression

Practical Guide to Logistic Regression

Bayesian Models for Astrophysical Data

Bayesian Models for Astrophysical Data

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