LEADER 04339nam 2200637 a 450 001 9911019274803321 005 20200520144314.0 010 $a9786613279965 010 $a9781283279963 010 $a1283279967 010 $a9781118165423 010 $a111816542X 010 $a9781118165430 010 $a1118165438 035 $a(CKB)2550000000053059 035 $a(EBL)818921 035 $a(SSID)ssj0000534179 035 $a(PQKBManifestationID)11333917 035 $a(PQKBTitleCode)TC0000534179 035 $a(PQKBWorkID)10508897 035 $a(PQKB)10103699 035 $a(MiAaPQ)EBC818921 035 $a(OCoLC)757511711 035 $a(Perlego)2751821 035 $a(EXLCZ)992550000000053059 100 $a19960209d1996 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAspects of statistical inference /$fA.H. Welsh 210 $aNew York $cWiley$dc1996 215 $a1 online resource (480 p.) 225 1 $aWiley series in probability and statistics 300 $a"A Wiley-Interscience publication." 311 08$a9780471115915 311 08$a0471115916 320 $aIncludes bibliographical references (p. 409-427) and indexes. 327 $aAspects of Statistical Inference; Contents; Preface; Acknowledgments; 1. Statistical Models; 1.1 Substantive Problems; 1.2 Initially Plausible Models; 1.3 Classes of Models; 1.4 Statistical Inference; 1.5 Informal Inferences; 1.6 Inductive Argument; 2. Bayesian, Fiducial and Likelihood Inference; 2.1 The Bayesian Paradigm; 2.2 Prior Distributions; 2.3 The Effect of Caffeine on the Volume of Urine; 2.4 Improper Priors; 2.5 Bayesian Hypothesis Testing; 2.6 Fiducial Theory; 2.7 Likelihood Theory; 3. Frequentist Inference; 3.1 Point Estimation; 3.2 Significance Tests; 3.3 Hypothesis Testing 327 $a3.4 Implementing Tests3.5 Likelihood Ratio, Likelihood, and Bayesian Tests; 3.6 Confidence Sets; 3.7 Confidence Sets from Discrete Data; 3.8 The Behrens-Fisher and Fieller-Creasy Problems; 3.9 Conditional Inference; 3.10 Simulation; 4. Large Sample Theory; 4.1 Approximate Confidence Intervals; 4.2 Multiparameter Problems; 4.3 The Choice of Inference Procedure; 4.4 Improving the Gaussian Approximation; 4.5 Hypothesis Testing; 4.6 Likelihood and Bayesian Theory; 5. Robust Inference; 5.1 The Standard Deviation; 5.2 Departures from Independence; 5.3 Robustness Theory 327 $a5.4 Bounded Influence Estimation5.5 Corrosion Resistance of Steel Plates; 5.6 Tests Based on M-estimators; 5.7 Other Approaches to Distributional Robustness; 5.8 Likelihood and Bayesian Theory; 6. Randomization and Resampling; 6.1 Experimental Design; 6.2 Randomization Models; 6.3 Randomization Tests; 6.4 The Randomization Basis for Gaussian Model-Based Tests; 6.5 Inference for Finite Populations; 6.6 Permutation Tests; 6.7 The Bootstrap; 6.8 Other Resampling Methods; 6.9 Nonparametric Methods; 7. Principles of Inference; 7.1 The Coherency Principle; 7.2 The Likelihood Principle 327 $a7.3 The Sufficiency Principle7.4 The Conditionality Principle; 7.5 The Development of the Likelihood Principle; 7.6 The Repeated Sampling Principle; 7.7 Other Principles; Appendix: Some Useful Facts; References; Author Index; Data and Analysis Index; Subject Index 330 $aRelevant, concrete, and thorough--the essential data-based text on statistical inferenceThe ability to formulate abstract concepts and draw conclusions from data is fundamental to mastering statistics. Aspects of Statistical Inference equips advanced undergraduate and graduate students with a comprehensive grounding in statistical inference, including nonstandard topics such as robustness, randomization, and finite population inference.A. H. Welsh goes beyond the standard texts and expertly synthesizes broad, critical theory with concrete data and relevant topics. The text foll 410 0$aWiley series in probability and statistics. 606 $aMathematical statistics 615 0$aMathematical statistics. 676 $a519.5/4 700 $aWelsh$b A. H$g(Alan H.),$f1960-$0252859 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019274803321 996 $aAspects of statistical inference$9626234 997 $aUNINA