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  • 1
    Buch
    Buch
    New York, NY [u.a.] :Springer,
    UID:
    almafu_BV002118846
    Umfang: XVI, 617 S. : , graph. Darst.
    Ausgabe: 2. ed.
    ISBN: 0-387-96098-8 , 3-540-96098-8 , 978-0-387-96098-2
    Serie: Springer series in statistics
    Anmerkung: Literaturverz. S. 571 - 598
    Früher: 1. Auflage Berger, James O. Statistical decision theory
    Sprache: Englisch
    Fachgebiete: Wirtschaftswissenschaften , Mathematik
    RVK:
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    Schlagwort(e): Statistische Entscheidungstheorie ; Bayes-Verfahren ; Bayes-Entscheidungstheorie ; CD-ROM ; Lehrbuch
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 2
    UID:
    b3kat_BV039608655
    Umfang: 1 Online-Ressource
    ISBN: 9781441969446
    Weitere Ausg.: Erscheint auch als Druckausgabe ISBN 978-1-4419-6943-9
    Sprache: Englisch
    Schlagwort(e): Statistische Entscheidungstheorie ; Bayes-Verfahren ; Festschrift
    URL: Volltext  (lizenzpflichtig)
    Mehr zum Autor: Chen, Ming-Hui 1961-
    Bibliothek Standort Signatur Band/Heft/Jahr Verfügbarkeit
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  • 3
    UID:
    almahu_9949198299502882
    Umfang: XIII, 537 p. 36 illus. , online resource.
    Ausgabe: 1st ed. 1994.
    ISBN: 9781461226185
    Inhalt: 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.
    Anmerkung: 1: Restrospective on Wald's Decision Theory and Sequential Analysis -- Minimaxity, More or Less -- A Retrospective of Wald's Sequential Analysis - Its Relation to Change-point Detection and Sequential Clinical Trials -- Group Bayes Estimation of the Exponential Mean: A Retrospective View of the Wald Theory -- 2: Asymptotics and Nonparametrics -- Ibragimov - Hasminskii Models -- Penalized Likelihood Hazard Estimation: Algorithm and Examples -- Estimating Lifetime Distribution Under Different Sampling Plans -- Estimating the Mixing Density of a Mixture of Power Series Distributions -- Estimating Linear Functionals in Density Estimation Models -- On Robust Estimation In Case-Control Studies with Errors in Covariates -- 3: Bayesian Analysis -- Applications and Limitations of Robust Bayesian Bounds and Type II MLE -- Bayesian Estimation of Political Transition Matrices -- Using Historical Controls in Clinical Trials: Application to ECMO -- A Geometric Solution of the Bayesian E-Optimal Design Problem -- Bayesian Estimation for Censored Data: An Experiment in Sensitivity Analysis -- Stability and Convergence of the Posterior in Non-Regular Problems -- A Hierarchical Bayesian Analysis of Circular Data with Autoregressive Errors: Modeling the Mechanical Properties of Cortical Bone -- Discovery Sampling and Selection Models -- 4: Decision Theory and Selection Procedures -- Loss Functions for Set Estimation -- Double Sample Estimation When Cost Depends on the Parameter -- On Some Bayesian Solutions of the Neyman-Scott Problem -- Empirical Bayes Two-Stage Procedures for Selecting the Best Bernoulli Population Compared with a Control -- On the Differentiation of Certain Probabilities with Applications in Statistical Decision Theory -- Minimax Bayes, Asymptotic Minimax and Sparse Wavelet Priors -- Simulating the Power of Decision Rules Applied to Data From the HGPRT Gene Mutation Assay -- Analyzing Randomized Response Data with a Binomial Selection Procedure -- Improved Confidence Statements for the Usual Multivariate Normal Confidence Set -- Improving on Truncated Estimators -- Bayesian and Non-Bayesian Estimation Using Balanced Loss Functions -- 5: Probability and Probabilistic Structures -- Distributions Which Are Gaussian Convolutions -- An Infinite Dimensional Convolution Theorem -- How Many Geometric (p) Samples Does It Take to See All the Balls in a Box -- The Asymptotic Distribution of Point Charges on a Conducting Sphere -- Permutation Invariant Upper and Lower Probabilities -- Subgraph Approximations for Directed Graphical Models -- Lower Bounds on Expected Redundancy for Classes of Continuous Markov Sources -- 6: Sequential, Adaptive, and Filtering Problems -- Random Walks for Quantile Estimation -- Some Procedures for State Estimation of a Hidden Markov Chain with Two States -- Adaptive Estimation Via Martingales -- The Blackwell Prediction Algorithm for Infinite 0-1 Sequences, and a Generalization -- Some Statistical Problems in Multitarget Tracking -- A Generalized Parking Problem -- Search Methods and Observer Logics.
    In: Springer Nature eBook
    Weitere Ausg.: Printed edition: ISBN 9781461276098
    Weitere Ausg.: Printed edition: ISBN 9780387941431
    Weitere Ausg.: Printed edition: ISBN 9781461226192
    Sprache: Englisch
    Fachgebiete: Wirtschaftswissenschaften , Mathematik
    RVK:
    RVK:
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    Schlagwort(e): Konferenzschrift ; Konferenzschrift
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 4
    Online-Ressource
    Online-Ressource
    New York, NY :Springer New York :
    UID:
    almahu_9947362925702882
    Umfang: XV, 428 p. , online resource.
    ISBN: 9781475717273
    Serie: Springer Series in Statistics,
    Inhalt: 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.
    Anmerkung: 1 Basic Concepts -- 2 Utility and Loss -- 3 Prior Information and Subjective Probability -- 4 Bayesian Analysis -- 5 Minimax Analysis -- 6 Invariance -- 7 Preposterior and Sequential Analysis -- 8 Complete and Essentially Complete Classes -- Appendix 1. Common Statistical Densities -- I. Continuous -- II. Discrete -- Appendix 2. Technical Arguments from Chapter 4 -- I. Verification of Formulas (4.6) through (4.8) -- II. Verification of Formula (4.10) -- III. Verification of Formula (4.12) -- Appendix 3. Technical Arguments from Chapter 7 -- I. Verification of Formula (7.8) -- II. Verification of Formula (7.10) -- Notation and Abbreviations -- Author Index.
    In: Springer eBooks
    Weitere Ausg.: Printed edition: ISBN 9781475717297
    Sprache: Englisch
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  • 5
    Online-Ressource
    Online-Ressource
    New York, NY :Springer New York :
    UID:
    almahu_9947363197902882
    Umfang: XVI, 618 p. , online resource.
    Ausgabe: Second Edition.
    ISBN: 9781475742862
    Serie: Springer Series in Statistics,
    Inhalt: 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.
    Anmerkung: 1 Basic Concepts -- 2 Utility and Loss -- 3 Prior Information and Subjective Probability -- 4 Bayesian Analysis -- 5 Minimax Analysis -- 6 Invariance -- 7 Preposterior and Sequential Analysis -- 8 Complete and Essentially Complete Classes -- Appendix 1 Common Statistical Densities -- I Continuous -- II Discrete -- Appendix 2 Supplement to Chapter 4 -- II Development of (4.121) and (4.122) -- III Verification of Formula (4.123) -- Appendix 3 Technical Arguments from Chapter 7 -- I Verification of Formula (7.8) -- II Verification of Formula (7.10) -- Notation and Abbreviations -- Author Index.
    In: Springer eBooks
    Weitere Ausg.: Printed edition: ISBN 9781441930743
    Sprache: Englisch
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