scholarly journals The asymptotic normality of an adjusted least squares estimator in a multivariate vector errors-in-variables regression model

2014 ◽  
Vol 88 ◽  
pp. 175-190
Author(s):  
I. O. Sen’ko
Keyword(s):  
Asymptotic Normality ◽  
Regression Model ◽  
Least Squares ◽  
Least Squares Estimator ◽  
Errors In Variables ◽  
Adjusted Least Squares

Confidence Sets for the Coefficients Vector of a Linear Regression Model with Nonspherical Disturbances

1997 ◽  
Vol 13 (3) ◽  
pp. 406-429 ◽  
Author(s):  
Anoop Chaturvedi ◽  
Hikaru Hasegawa ◽  
Ajit Chaturvedi ◽  
Govind Shukla
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Least Squares ◽  
Linear Regression Model ◽  
Generalized Least Squares ◽  
Least Squares Estimator ◽  
Regression Coefficients ◽  
Confidence Sets ◽  
Generalized Least Squares Estimator ◽  
Coverage Probabilities

In this present paper, considering a linear regression model with nonspherical disturbances, improved confidence sets for the regression coefficients vector are developed using the Stein rule estimators. We derive the large-sample approximations for the coverage probabilities and the expected volumes of the confidence sets based on the feasible generalized least-squares estimator and the Stein rule estimator and discuss their ranking.

Sign in / Sign up

Export Citation Format

Share Document