Conditional specification of statistical models
By: Barry C. Arnold, Enrique Castillo, José María Sarabia.
Contributors: Castillo, Enrique, 1946-; Sarabia, José-Mariá.
Series: Springer series in statistics
Publisher: Springer
Published: 1999
Place of publication: New York
Language: eng
Number of pages: 411
Subjects: Distribution (Probability theory)
Description: "Any efforts to visualize multivariate densities will necessarily involve the use of cross sections or, equivalently, conditional densities. Distributions that are completely specified in terms of conditional densities are the focus of this book. They form flexible families of multivariate densities that provide natural extensions of many classical multivariate models. They are also used in any modeling situation where conditional information is completely or partially available.

In the context of eliciting appropriate priors for multiparameter problems in Bayesian analysis, conditionally specified distributions are particularly convenient. They are effectively tailor-made for Gibbs sampler posterior simulations. All researchers, not just Bayesians, seeking more flexible models than those provided by classical models will find conditionally specified distributions of interest."--BOOK JACKET.

"This book assumes an introductory course in statistical theory and some familiarity with calculus of several variables, matrix theory, and elementary Markov chain concepts."--BOOK JACKET.
Notes: Includes bibliographical references (p. [389]-398) and index.
Expanded, up-to-date version of 1992 ed.