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.

引用

页码：41 / 50

页数：10

共 5 条

[1] MAXIMUM-LIKELIHOOD ESTIMATION OF THE PARAMETERS OF A MULTIVARIATE NORMAL-DISTRIBUTION [J].