Methods of Statistical Model Estimation - Hilbe, Joseph; Robinson, Andrew
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This book 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. It presents algorithms for the estimation of a variety of useful 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.

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Produktbeschreibung
This book 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. It presents algorithms for the estimation of a variety of useful 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.
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Autorenporträt
Joseph M. Hilbe is a Solar System Ambassador with NASA's Jet Propulsion Laboratory at the California Institute of Technology, an adjunct professor of statistics at Arizona State University, and an Emeritus Professor at the University of Hawaii. An elected fellow of the American Statistical Association and elected member (fellow) of the International Statistical Institute, Professor Hilbe is president of the International Astrostatistics Association, editor-in-chief of two book series, and currently on the editorial boards of six journals in statistics and mathematics. He has authored twelve statistics texts, including Logistic Regression Models, two editions of the bestseller Negative Binomial Regression, and two editions of Generalized Estimating Equations (with J. Hardin). Andrew P. Robinson is Deputy Director of the Australian Centre for Excellence in Risk Analysis with the Department of Mathematics and Statistics at the University of Melbourne. He has coauthored the popular Forest Analytics with R and the best-selling Introduction to Scientific Programming and Simulation using R. Dr. Robinson is the author of "IcebreakeR," a well-received introduction to R that is freely available online. With Professor Hilbe, he authored the R COUNT and MSME packages, both available on CRAN. He has also presented at numerous workshops on R programming to the scientific community.