Description
This text discusses the various methods used to estimate parameters for statistical models and provides informative model summary statistics. It is designed for R users and those interested in understanding the algorithms used for statistical model fitting. The book covers algorithms for regression procedures, including OLS regression, generalized linear models, and maximum likelihood models. It also includes a random effects model and a Bayesian Poisson model. The book's coverage is innovative in that it uses executable computer code to present and connect theoretical content, and focuses on the performance of statistical estimation rather than algebraic details. It is recommended as a reference book for graduate courses on statistical models and for understanding the algorithm used in statistical model fitting.
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. Review: This book is a concise volume of statistical methods associated with parametric models. With a rich set of R codes, the book contains full demonstration of how to apply the parametric statistical models to obtain desired results of analyses with minimal theoretical details. ... a useful reference book for a graduate course on statistical models using a standard textbook ... many illustrative samples are truly easy to understand. This book is also handy for understanding the algorithm used in statistical model fitting, using the R programming language. -Jae-kwang Kim, Biometrics, March 2014
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