MAXIMUM-LIKELIHOOD-ESTIMATION FOR MULTIVARIATE NORMAL-DISTRIBUTION WITH MONOTONE SAMPLE

被引：30
|
作者：
JINADASA, KG
TRACY, DS
机构：
[1] ILLINOIS STATE UNIV,DEPT MATH,NORMAL,IL 61761
[2] UNIV WINDSOR,DEPT MATH & STAT,WINDSOR N9B 3P4,ONTARIO,CANADA
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 1992年 / 21卷 / 01期
关键词：
MULTIVARIATE NORMAL; MISSING DATA; MONOTONE SAMPLE; MAXIMUM LIKELIHOOD ESTIMATION; MATRIX DERIVATIVES;
D O I：
10.1080/03610929208830763
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：
Closed forms are obtained for the maximum likelihood estimators of the mean vector and the covariance matrix of a multivariate normal model with a k-step monotone missing data pattern. Matrix derivatives are used in the derivation. Our results extend those of Anderson and Olkin (1985) for the 2-step missing data pattern.
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页码：41 / 50
页数：10
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