Comparative Statistical Inference

Much has happened in the field of inference and decision making during the past decade or so. This fully updated and revised third edition of Comparative Statistical Inference presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision making. As in earlier editions, the material is set in a historical context to more powerfully illustrate the ideas and concepts.Includes fully updated and revised material from the successful second editionDiscusses all recent major developmentsParticular attention is given to the nature and importance of basic concepts (probability, utility, likelihood, etc.)Includes extensive references and bibliographRecent changes in emphasis, principle and methodology are carefully explained and evaluated. These include major developments in the use of modified forms of the likelihood function, the computational and interpretative advantages opened up by the Gibbs sampler and Markov Chain Monte Carlo methods, advances in predictive methods in classical Bayesian contexts (including the prequential approach) and broader incorporation of multiparameter issues. With the many additions and changes, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.

Spis treści:
Introduction: Statistical Inference and Decision–making.
An Illustration of the Different Approaches.
Probability.
Utility and Decision–making.
Classical Inference.
Bayesian Inference.
Decision Theory.
Other Approaches.
Perspective.
References.
Index.

Okładka tylna:
Much has happened in the field of inference and decision making during the past decade or so. This fully updated and revised third edition of Comparative Statistical Inference presents a wide ranging, balanced account of the fundamental issues across the f ull spectrum of inference and decision making. As in earlier editions, the material is set in a historical context to more powerfully illustrate the ideas and concepts.Includes fully updated and revised material from the successful second editionDiscusses all recent major developmentsParticular attention is given to the nature and importance of basic concepts (probability, utility, likelihood, etc.)Includes extensive references and bibliographRecent changes in emphasis, principle and methodology are carefully explained and evaluated. These include major developments in the use of modified forms of the likelihood function, the computational and interpretative advantages opened up by the Gibbs sampler and Markov Chain Monte Carlo methods, advances in predictive methods in classical Bayesian contexts (including the prequential approach) and broader incorporation of multiparameter issues. With the many additions and changes, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.