Statistical Decision Theory
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Introduction To Statistical Decision Theory
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Author : Silvia Bacci
language : en
Publisher: CRC Press
Release Date : 2019-07-11
Introduction To Statistical Decision Theory written by Silvia Bacci and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-11 with Mathematics categories.
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference. The book is specifically designed to appeal to students and researchers that intend to acquire a knowledge of statistical science based on decision theory. Features Covers approaches for making decisions under certainty, risk, and uncertainty Illustrates expected utility theory and its extensions Describes approaches to elicit the utility function Reviews classical and Bayesian approaches to statistical inference based on decision theory Discusses the role of causal analysis in statistical decision theory
Statistical Decision Theory And Bayesian Analysis
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Author : James O. Berger
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Statistical Decision Theory And Bayesian Analysis written by James O. Berger and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Mathematics categories.
In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.
Applied Statistical Decision Theory
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Author : Howard Raiffa
language : en
Publisher: John Wiley & Sons
Release Date : 2000-06-02
Applied Statistical Decision Theory written by Howard Raiffa and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-06-02 with Mathematics categories.
"In the field of statistical decision theory, Raiffa and Schlaifer have sought to develop new analytic techniques by which the modern theory of utility and subjective probability can actually be applied to the economic analysis of typical sampling problems." —From the foreword to their classic work Applied Statistical Decision Theory. First published in the 1960s through Harvard University and MIT Press, the book is now offered in a new paperback edition from Wiley
Introduction To Statistical Decision Theory
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Author : John Winsor Pratt
language : en
Publisher: MIT Press
Release Date : 1995
Introduction To Statistical Decision Theory written by John Winsor Pratt and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Business & Economics categories.
They then examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes.
Statistical Decision Theory
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Author : Lionel Weiss
language : en
Publisher:
Release Date : 1961
Statistical Decision Theory written by Lionel Weiss and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1961 with Linear programming categories.
Statistical Decision Theory And Related Topics Iii
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Author : Shanti Swarup Gupta
language : en
Publisher:
Release Date : 1982
Statistical Decision Theory And Related Topics Iii written by Shanti Swarup Gupta and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with categories.
Theory Of Games And Statistical Decisions
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Author : David A. Blackwell
language : en
Publisher: Courier Corporation
Release Date : 2012-06-14
Theory Of Games And Statistical Decisions written by David A. Blackwell and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-14 with Mathematics categories.
Evaluating statistical procedures through decision and game theory, as first proposed by Neyman and Pearson and extended by Wald, is the goal of this problem-oriented text in mathematical statistics. First-year graduate students in statistics and other students with a background in statistical theory and advanced calculus will find a rigorous, thorough presentation of statistical decision theory treated as a special case of game theory. The work of Borel, von Neumann, and Morgenstern in game theory, of prime importance to decision theory, is covered in its relevant aspects: reduction of games to normal forms, the minimax theorem, and the utility theorem. With this introduction, Blackwell and Professor Girshick look at: Values and Optimal Strategies in Games; General Structure of Statistical Games; Utility and Principles of Choice; Classes of Optimal Strategies; Fixed Sample-Size Games with Finite Ω and with Finite A; Sufficient Statistics and the Invariance Principle; Sequential Games; Bayes and Minimax Sequential Procedures; Estimation; and Comparison of Experiments. A few topics not directly applicable to statistics, such as perfect information theory, are also discussed. Prerequisites for full understanding of the procedures in this book include knowledge of elementary analysis, and some familiarity with matrices, determinants, and linear dependence. For purposes of formal development, only discrete distributions are used, though continuous distributions are employed as illustrations. The number and variety of problems presented will be welcomed by all students, computer experts, and others using statistics and game theory. This comprehensive and sophisticated introduction remains one of the strongest and most useful approaches to a field which today touches areas as diverse as gambling and particle physics.
Statistical Decision Theory
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Author : James Berger
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Statistical Decision Theory written by James Berger and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Mathematics categories.
Decision theory is generally taught in one of two very different ways. When of opti taught by theoretical statisticians, it tends to be presented as a set of mathematical techniques mality principles, together with a collection of various statistical procedures. When useful in establishing the optimality taught by applied decision theorists, it is usually a course in Bayesian analysis, showing how this one decision principle can be applied in various practical situations. The original goal I had in writing this book was to find some middle ground. I wanted a book which discussed the more theoretical ideas and techniques of decision theory, but in a manner that was constantly oriented towards solving statistical problems. In particular, it seemed crucial to include a discussion of when and why the various decision prin ciples should be used, and indeed why decision theory is needed at all. This original goal seemed indicated by my philosophical position at the time, which can best be described as basically neutral. I felt that no one approach to decision theory (or statistics) was clearly superior to the others, and so planned a rather low key and impartial presentation of the competing ideas. In the course of writing the book, however, I turned into a rabid Bayesian. There was no single cause for this conversion; just a gradual realization that things seemed to ultimately make sense only when looked at from the Bayesian viewpoint.
Statistical Decision Theory And Related Topics
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Author :
language : en
Publisher:
Release Date : 1982
Statistical Decision Theory And Related Topics written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1982 with categories.
Frontiers Of Statistical Decision Making And Bayesian Analysis
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Author : Ming-Hui Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-24
Frontiers Of Statistical Decision Making And Bayesian Analysis written by Ming-Hui Chen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-24 with Mathematics categories.
Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.