Methods of statistical model estimation
 Responsibility
 Joseph M. Hilbe, Andrew P. Robinson.
 Language
 English.
 Publication
 Boca Raton, FL : CRC Press, [2013]
 Physical description
 xii, 243 pages : illustrations ; 25 cm
Access
Creators/Contributors
 Author/Creator
 Hilbe, Joseph M., 1944
 Contributor
 Robinson, Andrew (Andrew P.)
Contents/Summary
 Bibliography
 Includes bibliographical references (pages 233237) and index.
 Contents

 Programming and R Introduction R Specifics Programming Making R Packages Further Reading Statistics and LikelihoodBased Estimation Introduction Statistical Models Maximum Likelihood Estimation Interval Estimates Simulation for Fun and Profit Ordinary Regression Introduction LeastSquares Regression MaximumLikelihood Regression Infrastructure Conclusion Generalized Linear Models Introduction GLM: Families and Terms The Exponential Family The IRLS Fitting Algorithm Bernoulli or Binary Logistic Regression Grouped Binomial Models Constructing a GLM Function GLM Negative Binomial Model Offsets Dispersion, Over and Under GoodnessofFit and Residual Analysis Weights Conclusion Maximum Likelihood Estimation Introduction MLE for GLM TwoParameter MLE Panel Data What Is a Panel Model? FixedEffects Model RandomIntercept Model Handling More Advanced Models The EM Algorithm Further Reading Model Estimation Using Simulation Simulation: Why and When? Synthetic Statistical Models Bayesian Parameter Estimation Discussion Bibliography Index Exercises appear at the end of each chapter.
 (source: Nielsen Book Data)
 Publisher's Summary
 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 twoparameter 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 MetropolisHastings 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.
(source: Nielsen Book Data)  Supplemental links
 Cover image:
Subjects
 Subject
 Estimation theory.
Bibliographic information
 Publication date
 2013
 Note
 "A Chapman & Hall book"
 ISBN
 9781439858028 (hardback)
 1439858020 (hardback)