Efficient and Adaptive Estimation for Semiparametric Models (Johns Hopkins Studies in the Mathematical Sciences)

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 copyright Book News, Inc. Portland, Or.

"Provides a comprehensive introduction and summary of the current state of understanding of the vast extension of the theory of regular parametric estimation to the case in which the parameter space is divided into a finite-dimensional and an infinite-dimensional component... An essential source book for anyone wishing to do research in this exciting area." -- Mathematical Reviews

"Makes an important contribution to modern mathematical statistics. The authors have done a fine job making this difficult area accessible for a broader audience. They have achieved this by including an abundance and variety of illustrative and important examples... Required reading for anyone interested in semiparametric models." -- Anton Schick, Journal of the American Statistical Association

"A thorough and deep account of the current state of play and is an essential source book for anyone wishing to do research in this exciting area." -- Jack Cuzick, Mathematical Reviews