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: eivknownvaru eivknownratue

Macro: eivknownatt
Description: eivknownatt presents the moment estimates of the parameters in the measurement error models, which has only one variable x. The degree of attenuation (also called reliability ratio) is known. All of the variables obey normal distributions. See Fuller(1987), section 1.1

Link:
Usage: {mux,beta1,beta0,sigmax,sigmau,sigmae} = eivknownatt(X,Y,kxx)
Input:
X n x 1 matrix, the design variables
Y n x 1 matrix, the response
kxx scalar, the degree of attenuation
Output:
mux scalar, the mean value of X
beta1 scalar, the estimate
beta0 scalar, the estimate
sigmax scalar, the estimate of the variance of X
sigmau scalar, the estimate of the variance of u
sigmae scalar, the estimate of the variance of e

Example:
library("eiv")
n = 100
randomize(n) 
x= normal(n)*9
w=x+9*normal(n)
Y =0.9+0.8*x+0.01*normal(n)
kwx =0.5
gest=eivknownatt(w,Y,kwx)
gest.mux        ; the estimate of the mean of X
gest.beta1      ; the estimate of b (true value is 0.8)
gest.beta0      ; the estimate of a (true value is 0.9)
gest.sigmax     ; the estimate of the variance of x
gest.sigmau     ; the estimate of the variance of u
gest.sigmae     ; the estimate of the variance of e
Result:
gest.mux=-0.93396; gest.beta1=0.7853
gest.beta0=0.31926; gest.sigmax=72.585
gest.sigmau=72.585; gest.sigmae=8.2323

Library: eiv
See also: eivknownvaru eivknownratue

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, 961212
(C) MD*TECH Method and Data Technologies, 28.6.1999