Methods of Statistical Model Estimation provides readers with an examination of the major methods used by researchers and programmers to estimate statistical model parameters and associated statistics. Designed for R programmers, thebook is also suitable for anyone wanting to better understand the optimization algorithms used for model estimation. The text focuses on R programming codefor 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 code is constructed in the book for each of the discussed methods of estimation, including working code for OLS regression, a near complete generalized linear models function, one- and two-parameter maximum likelihood models for both pooled and panel models, a random effects model estimated using the EM algorithm, and a Bayesian Poisson model using Metropolis-Hastings sampling. The authors also discuss a number of ancillaryissues. They present a step-by-step method for developing R packages, an examination of the basics of R programming and numerical optimization, and an overview of R's object oriented programming capabilities. This unique book will beuseful to R programmers for many years to come.
- ISBN: 978-1-4398-5802-8
- Editorial: Chapman Hall/CRC
- Encuadernacion: Cartoné
- Páginas: 320
- Fecha Publicación: 15/01/2013
- Nº Volúmenes: 1
- Idioma: Inglés