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Methods of Statistical Model Estimation
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.
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.
256 pages, Hardcover
First published September 15, 2012
1 person is currently reading
2 people want to read
About the author
Andrew Robinson
460 books75 followers(William) Andrew Coulthard Robinson is a British author and former newspaper editor.
Andrew Robinson was educated at the Dragon School, Eton College where he was a King's Scholar, University College, Oxford where he read Chemistry and finally the School of Oriental and African Studies in London. He is the son of Neville Robinson, an Oxford physicist.
Robinson first visited India in 1975 and has been a devotee of the country's culture ever since, in particular the Bengali poet and philosopher Rabindranath Tagore and the Bengali film director Satyajit Ray. He has authored many books and articles. Until 2006, he was the Literary Editor of the Times Higher Education Supplement<?em>. He has also been a Visiting Fellow at Wolfson College, Cambridge.
He is based in London and is now a full-time writer.
Librarian Note: There is more than one author by this name in the Goodreads data base.
Andrew Robinson was educated at the Dragon School, Eton College where he was a King's Scholar, University College, Oxford where he read Chemistry and finally the School of Oriental and African Studies in London. He is the son of Neville Robinson, an Oxford physicist.
Robinson first visited India in 1975 and has been a devotee of the country's culture ever since, in particular the Bengali poet and philosopher Rabindranath Tagore and the Bengali film director Satyajit Ray. He has authored many books and articles. Until 2006, he was the Literary Editor of the Times Higher Education Supplement<?em>. He has also been a Visiting Fellow at Wolfson College, Cambridge.
He is based in London and is now a full-time writer.
Librarian Note: There is more than one author by this name in the Goodreads data base.
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