Pastāsti draugiem par šo preci:
Methods of Statistical Model Estimation Joseph Hilbe 1. izdevums
Pasūtīts no attālās noliktavas
Mūsu klienti saka:
Pieejams arī kā:
Methods of Statistical Model Estimation
Joseph Hilbe
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.
255 pages, 13 black & white illustrations, 13 black & white tables
| Mediji | Grāmatas Hardcover Book (Grāmata ar cieto muguriņu un vāku) |
| Izlaists | 2013. gada 28. maijs |
| ISBN13 | 9781439858028 |
| Izdevēji | Taylor & Francis Inc |
| Lapas | 255 |
| Izmēri | 176 × 236 × 21 mm · 556 g |
| Valoda | Angļu |
