Applied Statistical Decision Theory
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Applied Statistical Decision Theory
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Author : Howard Raiffa
language : en
Publisher: Wiley-Interscience
Release Date : 2000-06-02
Applied Statistical Decision Theory written by Howard Raiffa and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-06-02 with Mathematics categories.
Das definitive Buch zur Anwendung der Bayes-Statistik auf wirtschaftliche Probleme in der Praxis, bei denen es um Entscheidungen mit unsicheren Randbedingungen geht! Der Aktionsplan als Ziel der Analyse soll sowohl den Prioritäten Rechnung tragen, die der Entscheidungsfinder bei den Folgen setzt, als auch unbekannte Faktoren in Form von Wahrscheinlichkeiten enthalten. - Jetzt als preiswerte Paperback-Ausgabe! (08/00)
Applied Statistical Decision Theory
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Author : Howard Raiffa
language : en
Publisher:
Release Date : 1974
Applied Statistical Decision Theory written by Howard Raiffa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1974 with categories.
Optimal Statistical Decision Bayesian Inference In Statistical Analysis Applied Statistical Decision Theory
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Author : Morris H. DeGroot
language : en
Publisher: Wiley
Release Date : 2006-05-19
Optimal Statistical Decision Bayesian Inference In Statistical Analysis Applied Statistical Decision Theory written by Morris H. DeGroot and has been published by Wiley this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-19 with Mathematics categories.
Set that includes three works covering statistical decision theory and analysis The three books within this set are Optimal Statistical Decisions, Bayesian Inference in Statistical Analysis, and Applied Statistical Decision Theory. Optimal Statistical Decisions discusses the theory and methodology of decision-making in the field. The volume stands as a clear introduction to Bayesian statistical decision theory. A second book, Bayesian Inference in Statistical Analysis, examines the application and relevance of Bayes' theorem to problems that occur during scientific investigations, where inferences must be made regarding parameter values about which little is known. Key aspects of the Bayesian approach are discussed, including the choice of prior distribution, the problem of nuisance parameters, and the role of sufficient statistics. Applied Statistical Decision Theory covers the development of analytic techniques in the field of statistical decision theory. This classic book was first published in the 1960s.
Applied Statistical Decision Theory
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Author : Howard Raiffa
language : en
Publisher:
Release Date : 1966
Applied Statistical Decision Theory written by Howard Raiffa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1966 with categories.
Statistical Decision Theory
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Author : Nicholas T. Longford
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-10-17
Statistical Decision Theory written by Nicholas T. Longford 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 2013-10-17 with Mathematics categories.
This monograph presents a radical rethinking of how elementary inferences should be made in statistics, implementing a comprehensive alternative to hypothesis testing in which the control of the probabilities of the errors is replaced by selecting the course of action (one of the available options) associated with the smallest expected loss. Its strength is that the inferences are responsive to the elicited or declared consequences of the erroneous decisions, and so they can be closely tailored to the client’s perspective, priorities, value judgments and other prior information, together with the uncertainty about them.
Applied Statistics In Decision Making
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Author : George Kuttickal Chacko
language : en
Publisher: Elsevier Publishing Company
Release Date : 1971
Applied Statistics In Decision Making written by George Kuttickal Chacko and has been published by Elsevier Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with Mathematics categories.
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 V
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Author : Shanti S. Gupta
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Statistical Decision Theory And Related Topics V written by Shanti S. Gupta 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 2012-12-06 with Business & Economics categories.
The Fifth Purdue International Symposium on Statistical Decision The was held at Purdue University during the period of ory and Related Topics June 14-19,1992. The symposium brought together many prominent leaders and younger researchers in statistical decision theory and related areas. The format of the Fifth Symposium was different from the previous symposia in that in addition to the 54 invited papers, there were 81 papers presented in contributed paper sessions. Of the 54 invited papers presented at the sym posium, 42 are collected in this volume. The papers are grouped into a total of six parts: Part 1 - Retrospective on Wald's Decision Theory and Sequential Analysis; Part 2 - Asymptotics and Nonparametrics; Part 3 - Bayesian Analysis; Part 4 - Decision Theory and Selection Procedures; Part 5 - Probability and Probabilistic Structures; and Part 6 - Sequential, Adaptive, and Filtering Problems. While many of the papers in the volume give the latest theoretical developments in these areas, a large number are either applied or creative review papers.
Decision Making Under Uncertainty
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Author : George K. Chacko
language : en
Publisher: Praeger
Release Date : 1991
Decision Making Under Uncertainty written by George K. Chacko and has been published by Praeger this book supported file pdf, txt, epub, kindle and other format this book has been release on 1991 with Business & Economics categories.
In real-life decision-making situations it is necessary to make decisions with incomplete information, for oftentimes uncertain results. In Decision-Making Under Uncertainty, Dr. Chacko applies his years of statistical research and experience to the analysis of twenty-four real-life decision-making situations, both those with few data points (eg: Cuban Missile Crisis), and many data points (eg: aspirin for heart attack prevention). These situations encompass decision-making in a variety of business, social and political, physical and biological, and military environments. Though different, all of these have one characteristic in common: their outcomes are uncertain/unkown, and unknowable. Chacko Demonstrates how the decision-maker can reduce uncertainty by choosing probable outcomes using the statistical methods he introduces. This detailed volume develops standard statistical concepts (t, x2, normal distribution, ANOVA), and the less familiar concepts (logical probability, subjective probability, Bayesian Inference, Penalty for Non-Fulfillment, Bluff-Threats Matrix, etc.). Chacko also offers a thorough discussion of the underlying theoretical principles. The end of each chapter contains a set of questions, three quarters of which focus on concepts, formulation, conclusion, resource commitments, and caveats; only one quarter with computations. Ideal for the practitioner, the work is also designed to serve as the primary text for graduate or advanced undergraduate courses in statistics and decision science.
Introduction To Statistical Decision Theory
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Author : John Pratt
language : en
Publisher: MIT Press
Release Date : 2008-01-25
Introduction To Statistical Decision Theory written by John 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 2008-01-25 with Business & Economics categories.
The Bayesian revolution in statistics—where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine—is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty. Starting with an extensive account of the foundations of decision theory, the authors develop the intertwining concepts of subjective probability and utility. They then systematically and comprehensively examine the Bernoulli, Poisson, and Normal (univariate and multivariate) data generating processes. For each process they consider how prior judgments about the uncertain parameters of the process are modified given the results of statistical sampling, and they investigate typical decision problems in which the main sources of uncertainty are the population parameters. They also discuss the value of sampling information and optimal sample sizes given sampling costs and the economics of the terminal decision problems. Unlike most introductory texts in statistics, Introduction to Statistical Decision Theory integrates statistical inference with decision making and discusses real-world actions involving economic payoffs and risks. After developing the rationale and demonstrating the power and relevance of the subjective, decision approach, the text also examines and critiques the limitations of the objective, classical approach.