Semiparametric Theory and Missing Data

Anastasios A. Tsiatis - Collection Springer Series In Statistics

400 pages, parution le 31/07/2006

Ajouter à une liste

Indisponible

Résumé

This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.

Written for: Researchers

Sommaire

Introduction to semiparametric models
Hilbert space for random vectors
The geometry of influence functions
Semiparametric models
Other examples of semiparametric models
Models and methods for missing data
Missing and coarsening at random for semiparametric models
The nuisance tangent space and its orthogonal complement
Augmented inverse probability weighted complete case estimators
Improving efficiency and double-robustness with coarsened data
Locally-efficient estimators for coarsened data semiparametric models
Approximate methods for gaining efficiency
Double robust estimator of the average causal treatment effect
Multiple imputation: a frequentist perspective

Voir tout

Replier

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Anastasios A. Tsiatis
Collection	Springer Series In Statistics
Parution	31/07/2006
Nb. de pages	400
Format	16 x 24
Couverture	Relié
Poids	685g
Intérieur	Noir et Blanc
EAN13	9780387324487
ISBN13	978-0-387-32448-7