Methods of Statistical Model Estimation - Joseph Hilbe,Andrew Robinson
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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 est ... Full description
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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.
More Information
| Author | Joseph Hilbe, Andrew Robinson |
|---|---|
| Publisher | Taylor & Francis Ltd (Sales) |
| Release year | 2013 |
| Cover type | Hardcover |
| EAN | 9781439858028 |