Statistics

Indeks : p.353 - 357 ; p.345 - 351 ; This book covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. It covers models that are most commonly used in social science research including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models, and it thoroughly develops each real-data example in painstaking detail. It provides a detailed introduction to mathematical statistics and teh Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods including the Gibbs sampler and the Metropolis-Hastings algorithm are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.