Efficient and Adaptive Estimation for Semiparametric Models

by Peter J. Bickel, Chris A.J. Klaassen, Ya'acov Ritov, Jon A. Wellner

Overview

This book is about estimation in situations where we believe we have enough knowledge to model some features of the data parametrically, but are unwilling to assume anything for other features. Such models have arisen in a wide variety of contexts in recent years, particularly in economics, epidemiology, and astronomy. The complicated structure of these models typically requires us to consider nonlinear estimation procedures which often can only be implemented algorithmically. The theory of these procedures is necessarily based on asymptotic approximations.

Product Details

ISBN-13:	9780387984735
Publisher:	Springer New York
Publication date:	05/08/1998
Series:	1384
Edition description:	1998
Pages:	560
Product dimensions:	6.10(w) x 9.10(h) x 1.40(d)

Table of Contents

Introduction.- Asymptotic Inference for (Finite-Dimensional) Parametric Models.- Information Bounds for Euclidean Parameters in Infinite-Dimensional Models.- Euclidean Parameters: Further Examples.- Information Bounds for Infinite-Dimensional Parameters.- Infinite-Dimensional Parameters: Further Examples: Construction of Examples.

Editorial Reviews

Originating with the 1983 Mathematical Sciences Lectures at Johns Hopkins given by Peter J. Bickel and Jon A. Wellner, this volume is about estimation in situations where enough is known to model some features of the data parametrically but not enough is known to assume anything for other features. Such models have arisen in a wide variety of contexts in recent years, particularly in economics, epidemiology, and astronomy. The focus is on asymptotic theory, and the scope is limited to models for independent, identically distributed observations. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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