LEADER 04164nam 22006494a 450 001 9910830797003321 005 20230829003414.0 010 $a1-280-28810-8 010 $a9786610288106 010 $a0-470-24458-5 010 $a0-471-74313-5 010 $a0-471-74312-7 035 $a(CKB)1000000000239335 035 $a(EBL)243673 035 $a(OCoLC)70701307 035 $a(SSID)ssj0000204680 035 $a(PQKBManifestationID)11171138 035 $a(PQKBTitleCode)TC0000204680 035 $a(PQKBWorkID)10188050 035 $a(PQKB)11560169 035 $a(MiAaPQ)EBC243673 035 $a(EXLCZ)991000000000239335 100 $a20050218d2006 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModes of parametric statistical inference$b[electronic resource] /$fSeymour Geisser with the assistance of Wesley Johnson 210 $aHoboken, N.J. $cWiley-Interscience$dc2006 215 $a1 online resource (218 p.) 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record. 311 $a0-471-66726-9 320 $aIncludes bibliographical references and index. 327 $a4.2 Remarks on Size4.3 Uniformly Most Powerful Tests; 4.4 Neyman-Pearson Fundamental Lemma; 4.5 Monotone Likelihood Ratio Property; 4.6 Decision Theory; 4.7 Two-Sided Tests; References; 5. Unbiased and Invariant Tests; 5.1 Unbiased Tests; 5.2 Admissibility and Tests Similar on the Boundary; 5.3 Neyman Structure and Completeness; 5.4 Invariant Tests; 5.5 Locally Best Tests; 5.6 Test Construction; 5.7 Remarks on N-P Theory; 5.8 Further Remarks on N-P Theory; 5.9 Law of the Iterated Logarithm (LIL); 5.10 Sequential Analysis; 5.11 Sequential Probability Ratio Test (SPRT); References 327 $a6. Elements of Bayesianism6.1 Bayesian Testing; 6.2 Testing a Composite vs. a Composite; 6.3 Some Remarks on Priors for the Binomial; 6.4 Coherence; 6.5 Model Selection; References; 7. Theories of Estimation; 7.1 Elements of Point Estimation; 7.2 Point Estimation; 7.3 Estimation Error Bounds; 7.4 Efficiency and Fisher Information; 7.5 Interpretations of Fisher Information; 7.6 The Information Matrix; 7.7 Sufficiency; 7.8 The Blackwell-Rao Result; 7.9 Bayesian Sufficiency; 7.10 Maximum Likelihood Estimation; 7.11 Consistency of the MLE; 7.12 Asymptotic Normality and "Efficiency" of the MLE 327 $a7.13 Sufficiency PrinciplesReferences; 8. Set and Interval Estimation; 8.1 Confidence Intervals (Sets); 8.2 Criteria for Confidence Intervals; 8.3 Conditioning; 8.4 Bayesian Intervals (Sets); 8.5 Highest Probability Density (HPD) Intervals; 8.6 Fiducial Inference; 8.7 Relation Between Fiducial and Bayesian Distributions; 8.8 Several Parameters; 8.9 The Fisher-Behrens Problem; 8.10 Confidence Solutions; 8.11 The Fieller-Creasy Problem; References; References; Index 330 $aA fascinating investigation into the foundations of statistical inferenceThis publication examines the distinct philosophical foundations of different statistical modes of parametric inference. Unlike many other texts that focus on methodology and applications, this book focuses on a rather unique combination of theoretical and foundational aspects that underlie the field of statistical inference. Readers gain a deeper understanding of the evolution and underlying logic of each mode as well as each mode's strengths and weaknesses.The book begins with fascinating highlights from 410 0$aWiley series in probability and statistics. 606 $aProbabilities 606 $aMathematical statistics 606 $aDistribution (Probability theory) 615 0$aProbabilities. 615 0$aMathematical statistics. 615 0$aDistribution (Probability theory) 676 $a519.5/4 676 $a519.54 700 $aGeisser$b Seymour$0102951 701 $aJohnson$b Wesley O$01702464 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830797003321 996 $aModes of parametric statistical inference$94087011 997 $aUNINA