{
"item_title" : "Methods of Statistical Model Estimation",
"item_author" : [" Joseph Hilbe", "Andrew Robinson "],
"item_description" : "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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Overview
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.
This item is Non-Returnable
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Details
- ISBN-13: 9780367380007
- ISBN-10: 0367380005
- Publisher: CRC Press
- Publish Date: September 2019
- Dimensions: 9.21 x 6.14 x 0.54 inches
- Shipping Weight: 0.8 pounds
- Page Count: 255
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