Restricted regression estimation in measurement error models

Authors:
Shalabh;Gaurav Garg;Neeraj Misra
Affiliations:
Department of Mathematics and Statistics, Indian Institute of Technology, Kanpur 208016, India;Department of Mathematics and Statistics, Indian Institute of Technology, Kanpur 208016, India;Department of Mathematics and Statistics, Indian Institute of Technology, Kanpur 208016, India
Venue:
Computational Statistics & Data Analysis
Year:
2007

Citing 1
Cited 3

Consistent estimation of coefficients in measurement error models with replicated observations

Journal of Multivariate Analysis

Editorial: Total Least Squares and Errors-in-variables Modeling

Computational Statistics & Data Analysis
Use of prior information in the consistent estimation of regression coefficients in measurement error models

Journal of Multivariate Analysis
Restricted estimation in multivariate measurement error regression model

Journal of Multivariate Analysis

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Abstract

The problem of consistent estimation of the regression coefficients when some prior information about the regression coefficients is available is considered. Such prior information is expressed in the form of exact linear restrictions. The knowledge of covariance matrix of measurement errors that is associated with explanatory variables is used to construct the consistent estimators. Some consistent estimators are suggested which satisfy the exact linear restrictions also. Their asymptotic properties are derived and analytically analyzed under a multivariate ultrastructural model with not necessarily normally distributed measurement errors. The finite sample properties of the estimators are studied through a Monte-Carlo simulation experiment.