Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: eiv
See also: eivvec1

Quantlet: eivvec2
Description: eivvect2 calculates the maximum likelihood estimators of the parameters in the measurement error models when the entire error covariance structure is known or known up to a scalar multiple. This macro deals with the extension of the model considered in eivknownvaru.

Reference(s):

Link:
Usage: {hatbeta,sigmam)=eivvec2(w,y,Gamma)
Input:
w n x p matrix, the design variables
y n x 1 matrix, the response
Gamma (p+1) x (p+1) matrix
Output:
hatbeta vector, the estimate
sigmam scalar, the estimate

Example:

library("xplore")

library("eiv")

y=#(1.015, 1.120, 1.937, 1.743, 2.173)

w=0*matrix(5,3)

w[,1]=#(1,1,1,1)

w[,2]=#(3.442, 3.180,3.943, 3.983, 4.068)

w[,3]=#(0.477, 0.610, 0.505, 0.415, 0.620)

sig=0*matrix(4,4)

sig[,1]=#(0.5546, 0,-0.1079, -0.0691)

sig[,2]=#(0,0,0,0)

sig[,3]=#(-0.1079,0,0.2756,0.1247)

sig[,4]=#(-0.0691,0,0.1247,0.0878)

Gamma=0.01*sig

gest=eivvec2(w,y,Gamma)

gest.hatbeta

gest.sigmam

Result:

Contents of hatbeta

[1,] -2.7727e+13

[2,]  0.24253

[3,]        0 

Contents of sigmam

[1,]  0.030575 


Library: eiv
See also: eivvec1

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: Hua Liang, 970724
(C) MD*TECH Method and Data Technologies, 21.9.2000