Efficient estimation for incomplete multivariate data

被引：1

作者：

Jorgensen, Bent ^{[1
]}

Petersen, Hans Chr. ^{[1
]}

机构：

[1] Univ So Denmark, Dept Math & Comp Sci, DK-5230 Odense M, Denmark

来源：

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2012年 / 142卷 / 05期

关键词：

EM algorithm; Estimating function; Fisher scoring algorithm; Godambe information matrix; Missing data; REML estimation; MAXIMUM-LIKELIHOOD; INFORMATION MATRIX; REGRESSION-MODELS; MISSING VALUES; PARAMETERS;

D O I：

10.1016/j.jspi.2011.11.024

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We review the Fisher scoring and EM algorithms for incomplete multivariate data from an estimating function point of view, and examine the corresponding quasi-score functions under second-moment assumptions. A bias-corrected REML-type estimator for the covariance matrix is derived, and the Fisher, Godambe and empirical sandwich information matrices are compared. We make a numerical investigation of the two algorithms, and compare with a hybrid algorithm, where Fisher scoring is used for the mean vector and the EM algorithm for the covariance matrix. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：1215 / 1224

页数：10

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