This fully updated and revised third edition, presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision–making. Much has happened in this field since the second edition was published: for example, Bayesian inferential procedures have not only gained acceptance but are often the preferred methodology. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what assumptions they are based on, and the practical implications of such distinctions.
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 edition
- Recent changes in emphasis, principle and methodology are carefully explained and evaluated
- Discusses all recent major developments
- Particular attention is given to the nature and importance of basic concepts (probability, utility, likelihood etc)
- Includes extensive references and bibliography
Written by a well–known and respected author, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.
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 edition
- Discusses all recent major developments
- Particular attention is given to the nature and importance of basic concepts (probability, utility, likelihood, etc.)
- Includes extensive references and bibliograph
Recent 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.
