Skip to content
Library Home
Start Over
Research Databases
E-Journals
Course Reserves
Library Home
Login to library account
English
Deutsch
Español
Français
Italiano
日本語
Nederlands
Português
Português (Brasil)
中文(简体)
中文(繁體)
Türkçe
עברית
Gaeilge
Cymraeg
Ελληνικά
Català
Euskara
Русский
Čeština
Suomi
Svenska
polski
Dansk
slovenščina
اللغة العربية
বাংলা
Galego
Tiếng Việt
Hrvatski
हिंदी
Հայերէն
Українська
Language
Library Catalog
All Fields
Title
Author
Subject
Call Number
ISBN/ISSN
Find
Advanced Search
|
Browse
|
Search Tips
Parametric statistical theory...
Cite this
Text this
Email this
Print
Export Record
Export to RefWorks
Export to EndNoteWeb
Export to EndNote
Save to List
Permanent link
Parametric statistical theory / Johann Pfanzagl, with the assistance of R. Hamböker.
Saved in:
Bibliographic Details
Main Author:
Pfanzagl, J. (Johann)
Other Authors:
Hamböker, R.
Format:
eBook
Language:
English
Published:
Berlin ; New York :
W. de Gruyter,
1994.
Series:
De Gruyter textbook.
Subjects:
Mathematical statistics.
Mathematics.
mathematics.
applied mathematics.
MATHEMATICS
>
Probability & Statistics
>
General.
Mathematical statistics
Mathematics
Online Access:
Click for online access
Holdings
Description
Table of Contents
Similar Items
Staff View
Table of Contents:
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
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
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
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
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
Similar Items
Non-standard parametric statistical inference
by: Cheng, Russell
Published: (2017)
The joy of statistics : a treasury of elementary statistical tools and their applications
by: Selvin, S.
Published: (2019)
Statistics essentials for dummies.
by: Rumsey, Deborah J.
Published: (2019)
A panorama of statistics : perspectives, puzzles and paradoxes in statistics
by: Sowey, Eric
Published: (2017)
Statistics for scientists and engineers
by: Shanmugam, Ramalingam
Published: (2015)