Maximum penalized likelihood estimation

Volume 1 : Density Estimation

P.P.B. Eggermont, V.N. Lariccia

510 pages, parution le 15/07/2001

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Résumé

This text deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints such as unimodality and log-concavity. It is intended for graduate students in statistics, applied mathematics, and operations research, as well as for researchers and practitioners in the field.
The focal points are existence and uniqueness of the estimators, almost sure convergence rates for the L1 error, and data-driven smoothing parameter selection methods, including their practical performance. The reader will gain insight into some of the generally applicable technical tools from probability theory (discrete parameter martingales) and applied mathematics (boundary, value problems and integration by parts tricks.) Convexity and convex optimization, as applied to maximum penalized likelihood estimation, receive special attention.
The authors are with the Statistics Program of the Department of Food and Resource Economics in the College of Agriculture at the University of Delaware.

Contents

Parametric Maximum Likelihood Estimation
Parametric Maximum Likelihood Estimation in Action
Kernel Density Estimation
Maximum Likelihood Density Estimation
Monotone and Unimodal Densities
Choosing the Smoothing Parameter
Nonparametric Density Estimation in Action
Convex Minimization in Finite Dimensional Spaces
Convex Minimization in Infinite Dimensional Spaces
Convexity in Action

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	P.P.B. Eggermont, V.N. Lariccia
Parution	15/07/2001
Nb. de pages	510
Format	16 x 24
Couverture	Relié
Poids	875g
Intérieur	Noir et Blanc
EAN13	9780387952680
ISBN13	978-0-387-95268-0