Methods of Statistical Model Estimation

Methods of Statistical Model Estimation

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  • Provides a step-by-step guide to model estimation using R
  • Covers the four primary methods: optimization, maximum likelihood, quadrature, and simulation
  • Includes background on modeling and builds from basic models to more complex setups
  • Develops readers’ R skills
  • Contains many real data examples to illustrate the methods


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.

Table of Contents

Programming and R
R Specifics
Making R Packages
Further Reading

Statistics and Likelihood-Based Estimation
Statistical Models
Maximum Likelihood Estimation
Interval Estimates
Simulation for Fun and Profit

Ordinary Regression
Least-Squares Regression
Maximum-Likelihood Regression

Generalized Linear Models
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
Dispersion, Over and Under
Goodness-of-Fit and Residual Analysis

Maximum Likelihood Estimation
Two-Parameter MLE

Panel Data
What Is a Panel Model?
Fixed-Effects Model
Random-Intercept Model
Handling More Advanced Models
The EM Algorithm
Further Reading

Model Estimation Using Simulation
Simulation: Why and When?
Synthetic Statistical Models
Bayesian Parameter Estimation



Exercises appear at the end of each chapter.

Author Bio(s)

Editorial Reviews

"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

Downloads / Updates

Resource OS Platform Updated Description Instructions
Cross Platform June 04, 2013 R package for the book on CRAN, with all functions and datasets click on

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