LEADER 05463nam 2200685Ia 450 001 9910830145403321 005 20170815111739.0 010 $a1-282-30731-2 010 $a9786612307317 010 $a0-470-31779-5 010 $a0-470-85982-2 010 $a0-470-31695-0 010 $a0-585-31386-5 035 $a(CKB)111004366693152 035 $a(EBL)470418 035 $a(OCoLC)609849290 035 $a(SSID)ssj0000126357 035 $a(PQKBManifestationID)11141373 035 $a(PQKBTitleCode)TC0000126357 035 $a(PQKBWorkID)10030723 035 $a(PQKB)10330363 035 $a(MiAaPQ)EBC470418 035 $a(PPN)159326745 035 $a(EXLCZ)99111004366693152 100 $a19980922d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aComparative statistical inference$b[electronic resource] /$fVic Barnett 205 $a3rd ed. 210 $aChichester ;$aNew York $cWiley$dc1999 215 $a1 online resource (403 p.) 225 1 $aWiley series in probability and mathematical statistics 300 $aDescription based upon print version of record. 311 $a0-471-97643-1 320 $aIncludes bibliographical references (p. [337]-364) and index. 327 $aComparative Statistical Inference; Contents; Preface; Preface to Second Edition; Preface to Third Edition; Acknowledgements; Chapter 1. Introduction: Statistical Inference and Decision-making; 1.1 What is Statistics?; 1.2 Probability Models; 1.3 Relevant Information; 1.4 Statistical Inference and Decision-making; 1.5 Different Approaches; 1.6 Arbitrariness and Controversy; 1.7 Historical Comment and Further References; Chapter 2. An Illustration of the Different Approaches; 2.1 A Practical Example; 2.2 Sample Data as the Sole Source of Information: the Classical Approach; 2.2.1 Batch Quality 327 $a2.2.2 Component Lifetimes2.3 Relevant Prior Information: the Bayesian Approach; 2.3.1 Prior Information on Batch Quality; 2.3.2 Prior Attitudes about Component Lifetimes; 2.4 Costs and Consequences: Simple Decision Theory Ideas; 2.5 Comment and Comparisons; Chapter 3. Probability; 3.1 Types of Probability; 3.2 'Classical' Probability; 3.3 The Frequency View; 3.4 Logical Probability; 3.5 Subjective Probability; 3.6 Other Viewpoints; 3.6.1 Chaos; 3.6.2 Fuzzy Set Theory; 3.6.3 Risk, Uncertainty and Sensitivity Analysis; 3.7 Some Historical Background; 3.8 And So; 3.9 And Yet 327 $aChapter 4. Utility and Decision-making4.1 Setting a Value on Rewards and Consequences; 4.2 The Rational Expression of Preferences; 4.3 Preferences for Prospects and Mixtures of Prospects; 4.4 The Numerical Assessment of Prospects; 4.5 The Measurement of Utilities; 4.5.1 Formal Construction of Utilities; 4.5.2 Personal Expression of Utilities; 4.6 Decision-making; 4.7 The Utility of Money; 4.8 Comment: Mathematical Refinements: Distinctions of Attitude; Chapter 5. Classical Inference; 5.1 Basic Aims and Concepts; 5.1.1 Information and its Representation 327 $a5.2 Estimation and Testing Hypotheses-the Dual Aims5.3 Point Estimation; 5.3.1 Criteria for Point Estimators; 5.3.2 Optimum Estimators; 5.3.3 Methods of Constructing Estimators; 5.3.4 Estimating Several Parameters; 5.4 Testing Statistical Hypotheses; 5.4.1 Criteria for Hypothesis Tests; 5.4.2 Uniformly Most Powerful Tests; 5.4.3 Construction of Tests; 5.5 Region and Interval Estimates; 5.6 Ancillarity, Conditionality, Modified forms of Sufficiency and Likelihood; 5.6.1 The Sufficiency, Conditionality and Likelihood Principles; 5.6.2 Modified Likelihood Forms (Marginal, Partial, Profile, etc.) 327 $a5.7 Comment and Controversy5.7.1 Initial and Final Precision; 5.7.2 Prediction and Tolerance Regions; 5.7.3 Hypothesis Tests and Decisions; 5.7.4 Counter Criticism; Chapter 6. Bayesian Inference; 6.1 Thomas Bayes; 6.2 The Bayesian Method; 6.3 Particular Techniques; 6.4 Prediction in Bayesian Inference; 6.5 Prior Information; 6.5.1 Prior Ignorance; 6.5.2 Vague Prior Knowledge; 6.5.3 Substantial Prior Knowledge; 6.5.4 Conjugate Prior Distributions; 6.5.5 Quantifying Subjective Prior Information; 6.6 Computing Posterior Distributions; 6.7 Empirical Bayes' methods: Meta-prior Distributions 327 $a6.7.1 Empirical Bayes' Methods 330 $aThis fully updated and revised third edition, presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision-making. Much has happened in this field since the second edition was published: for example, Bayesian inferential procedures have not only gained acceptance but are often the preferred methodology. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what a 410 0$aWiley series in probability and mathematical statistics. 606 $aMathematical statistics 606 $aStatistical decision 615 0$aMathematical statistics. 615 0$aStatistical decision. 676 $a519.5 676 $a519.54 700 $aBarnett$b Vic$012011 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830145403321 996 $aComparative statistical inference$9118177 997 $aUNINA