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

Macro: 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. see Fuller (1987),page 124-126.

Link:
Usage: {hatbeta,sigmam)=eivvec2(X,Y,Gamma)
Input:
X 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("eiv")
library("xplore")
Y=#(1.015, 1.120, 1.937, 1.743, 2.173)
X=0*matrix(5,3)
X[,1]=#(1,1,1,1)
X[,2]=#(3.442, 3.180,3.943, 3.983, 4.068)
X[,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(X,Y,Gamma)
gest.hatbeta
gest.sigmam
Result:
gest.hatbeta=(-1.0843e+10, 0.96752, 0)
gest.sigmam=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, 28.6.1999