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|a QA276
|b .P477 1994eb
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|x 029000
|2 bisacsh
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|a 519.5/4
|2 20
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| 049 |
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|a UAMI
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| 100 |
1 |
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|a Pfanzagl, J.
|q (Johann)
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| 245 |
1 |
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|a Parametric statistical theory /
|c Johann Pfanzagl, with the assistance of R. Hamböker.
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| 260 |
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|a Berlin ;
|a New York :
|b W. de Gruyter,
|c 1994.
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| 300 |
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|a 1 online resource (xiii, 374 pages) :
|b illustrations
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a data file
|2 rda
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| 490 |
1 |
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|a De Gruyter textbook
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| 504 |
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|a Includes bibliographical references (pages 345-359)-and indexes.
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| 505 |
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|a Preface; Introduction; Chapter 1 Sufficiency and completeness; 1.1 Introduction; 1.2 Sufficiency and factorization of densities; 1.3 Sufficiency and exhaustivity; 1.4 Minimal sufficiency; 1.5 Completeness; 1.6 Exponential families; 1.7 Auxiliary results on families with monotone likelihood ratios; 1.8 Ancillary statistics; 1.9 Equivariance and invariance; 1.10 Appendix: Conditional expectations, conditional distributions; Chapter 2 The evaluation of estimators; 2.1 Introduction; 2.2 Unbiasedness of estimators; 2.3 The concentration of real valued estimators
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| 505 |
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|a 2.4 Concentration of multivariate estimators2.5 Evaluating estimators by loss functions; 2.6 The relative efficiency of estimators; 2.7 Examples on the evaluation of estimators; Chapter 3 Mean unbiased estimators and convex loss functions; 3.1 Introduction; 3.2 The Rao-Blackwell-Lehmann-Scheffé-Theorem; 3.3 Examples of mean unbiased estimators with minimal convex risk; 3.4 Mean unbiased estimation of probabilities; 3.5 A result on bounded mean unbiased estimators; Chapter 4 Testing hypotheses; 4.1 Basic concepts; 4.2 Critical functions, critical regions; 4.3 The Neyman-Pearson Lemma
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| 505 |
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|a 4.4 Optimal tests for composite hypotheses4.5 Optimal tests for families with monotone likelihood ratios; 4.6 Tests of Neyman structure; 4.7 Most powerful similar tests for a real parameter in the presence of a nuisance parameter; Chapter 5 Confidence procedures; 5.1 Basic concepts; 5.2 The evaluation of confidence procedures; 5.3 The construction of one-sided confidence bounds and median unbiased estimators; 5.4 Optimal one-sided confidence bounds and median unbiased estimators; 5.5 Optimal one-sided confidence bounds and median unbiased estimators in the presence of a nuisance parameter
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| 505 |
8 |
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|a 5.6 Examples of maximally concentrated confidence boundsChapter 6 Consistent estimators; 6.1 Introduction; 6.2 A general consistency theorem; 6.3 Consistency of M-estimators; 6.4 Consistent solutions of estimating equations; 6.5 Consistency of maximum likelihood estimators; 6.6 Examples of ML estimators; 6.7 Appendix: Uniform integrability, stochastic convergence and measurable selection; Chapter 7 Asymptotic distributions of estimator sequences; 7.1 Limit distributions; 7.2 How to deal with limit distributions; 7.3 Asymptotic confidence bounds; 7.4 Solutions to estimating equations
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| 505 |
8 |
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|a 7.5 The limit distribution of ML estimator sequences7.6 Stochastic approximations to estimator sequences; 7.7 Appendix: Weak convergence; Chapter 8 Asymptotic bounds for the concentration of estimators and confidence bounds; 8.1 Introduction; 8.2 Regular sequences of confidence bounds and median unbiased estimators; 8.3 Sequences of confidence bounds and median unbiased estimators with limit distributions; 8.4 The convolution theorem; 8.5 Maximally concentrated limit distributions; 8.6 Superefficiency; Chapter 9 Miscellaneous results on asymptotic distributions; 9.1 Examples of ML estimators
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| 546 |
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|a English.
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
|
0 |
|a Mathematical statistics.
|
| 650 |
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0 |
|a Mathematics.
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| 650 |
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6 |
|a Mathématiques.
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| 650 |
|
7 |
|a mathematics.
|2 aat
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| 650 |
|
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|a applied mathematics.
|2 aat
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| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
|
| 650 |
|
7 |
|a Mathematical statistics
|2 fast
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| 650 |
|
7 |
|a Mathematics
|2 fast
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| 700 |
1 |
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|a Hamböker, R.
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| 776 |
0 |
8 |
|i Print version:
|a Pfanzagl, J. (Johann).
|t Parametric statistical theory.
|d Berlin ; New York : W. de Gruyter, 1994
|w (DLC) 94021850
|
| 830 |
|
0 |
|a De Gruyter textbook.
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=937280
|z Texto completo
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