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by J.A. Wellner,P. Groeneboom
Download Information Bounds and Nonparametric Maximum Likelihood Estimation (Oberwolfach Seminars) fb2
Mathematics
  • Author:
    J.A. Wellner,P. Groeneboom
  • ISBN:
    3764327944
  • ISBN13:
    978-3764327941
  • Genre:
  • Publisher:
    Birkhäuser; 1992 edition (July 31, 1992)
  • Pages:
    128 pages
  • Subcategory:
    Mathematics
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    1168 kb
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    4.4
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Authors: Groeneboom, . Wellner, .

Authors: Groeneboom, . In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimation in several particular inverse problems: interval censoring and deconvolution models. Part I, based on Jon Wellner's lectures, gives a brief sketch of information lower bound theory: Hajek's convolution theorem and extensions, useful minimax bounds for parametric problems due to Ibragimov and Has'minskii, and a recent result characterizing differentiable functionals due to van der Vaart (1991).

Nonparametric Maximum Likelihood Estimation - Libro electrónico escrito por P. Groeneboom, .

Information Bounds and Nonparametric Maximum Likelihood Estimation - Libro electrónico escrito por P. This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990.

Автор: Groeneboom . Wellner . This book helps bridge this gap between applied economists and theoretical nonparametric econometricians.

Information Bounds and Nonparametric Maximum Likelihood Estimation. Wellner and P. Groeneboom.

oceedings{ationBA, title {Information bounds and nonparametric maximum likelihood estimation}, author {Piet Groeneboom and Jon A. Wellner}, year {1992} }. Piet Groeneboom, Jon A. I. Information Bounds. 1 Models, scores, and tangent spaces. . Models . Scores: Differentiability of the Model. Tangent Sets P0 and Tangent Spaces . Score Operators. 2 Convolution and asymptotic minimax theorems. Finite-dimensional Parameter Spaces.

Information Bounds and Nonparametric Maximum Likelihood Estimation (Oberwolfach Seminars) by P. Groeneboom (1992-07-31) P. Groeneboom; . Download Information Bounds and Nonparametric Maximum Likel. pdf Read Online Information Bounds and Nonparametric Maximum Lik. Download and Read Free Online Information Bounds and Nonparametric Maximum Likelihood Estimation (Oberwolfach Seminars) by P.

Wellner, Jon A. Series statement. Nonparametric statistics. John and Mary Nichols Rare Books and Special CollectionsBorrow it.

Groeneboom, P. Index. Wellner, Jon A. Information bounds and nonparametric maximum likelihood estimation, Piet Groeneboom, Jon A. Information bounds and nonparametric maximum likelihood estimation. Basel Boston, Birkhäuser, 1992. 401 W. Brooks S. Rm. 509NW, Norman, OK, 73019, US.

Nonparametric Maximum Likelihood. Chapter · July 2005 with 3 Reads. This article develops a new and stable estimator for information matrix when the EM algorithm is used in maximum likelihood estimation. DOI: 1. 002/0470011815. In book: Encyclopedia of Biostatistics. Cite this publication. The method works for dependent data sets and when the expectation step is an irregular function of the conditioning.

The first part deals with information lower bounds and differentiable functionals. The second part focuses on nonparametric maximum likelihood estimators for interval censoring and deconvolution. The distribution theory of these estimators is developed and new algorithms for computing them are introduced. Contents: Part I. Information Bounds: 1. Models, scores, and tangent spaces • 2. Convolution and asymptotic minimax theorems • 3. Van der Vaart’s Differentiability Theorem • PART II.

April 16, 2010 History. found in the catalog by P. Published 1992 by Birkhäuser in Basel, Boston. found in the catalog. 42 tax deductible donation. 1 2 3 4 5. Want to Read. Estimation theory, Factor analysis, Nonparametric statistics. There's no description for this book yet.

This book contains the lecture notes for a DMV course presented by the authors at Gunzburg, Germany, in September, 1990. In the course we sketched the theory of information bounds for non parametric and semiparametric models, and developed the theory of non parametric maximum likelihood estimation in several particular inverse problems: interval censoring and deconvolution models. Part I, based on Jon Wellner's lectures, gives a brief sketch of information lower bound theory: Hajek's convolution theorem and extensions, useful minimax bounds for parametric problems due to Ibragimov and Has'minskii, and a recent result characterizing differentiable functionals due to van der Vaart (1991). The differentiability theorem is illustrated with the examples of interval censoring and deconvolution (which are pursued from the estimation perspective in part II). The differentiability theorem gives a way of clearly distinguishing situations in which 1 2 the parameter of interest can be estimated at rate n / and situations in which this is not the case. However it says nothing about which rates to expect when the functional is not differentiable. Even the casual reader will notice that several models are introduced, but not pursued in any detail; many problems remain. Part II, based on Piet Groeneboom's lectures, focuses on non parametric maximum likelihood estimates (NPMLE's) for certain inverse problems. The first chapter deals with the interval censoring problem.