TESTING LINEAR HYPOTHESES IN ERRORS IN VARIABLES MODEL

被引：1

作者：

BANSAL, NK ^{[1
]}

机构：

[1] MARQUETTE UNIV,DEPT MATH STAT & COMP SCI,MILWAUKEE,WI 53233

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 1990年 / 42卷 / 03期

关键词：

Eigenvalues and eigenvectors; Jacobian of a transformation; likelihood function; likelihood ratio test; Poincaré separation theorem;

D O I：

10.1007/BF00049309

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：

A multivariate errors-in-variables model in the matrix form can be written as X=U+E, Y=UA′+WB+F, where X (n×p) and Y (n×q) are observed matrices, E and F are error matrices whose rows are normally distributed, W (n×k) is a known matrix of rank k, and U, A and B are unknown matrices. We consider the problems of testing linear hypotheses: (i) H0: AR=K and (ii) H0: S′A=L, where R, K, S and L are known matrices, and we derive the likelihood ratio tests for testing these hypotheses. © 1990 The Institute of Statistical Mathematics.

引用

页码：581 / 596

页数：16

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