Shortcuts
Top of page (Alt+0)
Page content (Alt+9)
Page menu (Alt+8)
Your browser does not support javascript, some WebOpac functionallity will not be available.
.
Default
.
PageMenu
-
Main Menu
-
Simple Search
.
Advanced Search
.
Journal Search
.
Refine Search Results
.
Preferences
.
Search Menu
Simple Search
.
Advanced Search
.
New Items Search
.
Journal Search
.
Refine Search Results
.
Bottom Menu
Help
Italian
.
English
.
German
.
New Item Menu
New Items Search
.
New Items List
.
Links
SISSA Library
.
ICTP library
.
Italian National web catalog (SBN)
.
Trieste University web catalog
.
Udine University web catalog
.
© LIBERO v6.4.1sp220816
Page content
You are here
:
Catalogue Card Display
Catalogue Card Display
RAK
Title: Information Bounds and Nonparametric Maximum Likelihood Estimation ([EBook] /) / by Piet Groeneboom, Jon A. Wellner. Dewey Class: 519.2 Author: Groeneboom, Piet. Added Personal Name: Wellner, Jon A. author. Publication: Basel : : Birkhäuser Basel : : Imprint: Birkhäuser,, 1992. Other name(s): SpringerLink (Online service) Physical Details: VIII, 128 p. : online resource. Series: DMV Seminar ;; 19 ISBN: 9783034886215 System details note: Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users) Summary Note: 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.: Contents note: I. Information Bounds -- 1 Models, scores, and tangent spaces -- 2 Convolution and asymptotic minimax theorems -- 3 Van der Vaart’s Differentiability Theorem -- II. Nonparametric Maximum Likelihood Estimation -- 1 The interval censoring problem -- 2 The deconvolution problem -- 3 Algorithms -- 4 Consistency -- 5 Distribution theory -- References. ------------------------------ *** There are no holdings for this record *** -----------------------------------------------
Quick Search
Search for