Paper Info
Title
Use of prior information in the consistent estimation of regression coefficients in measurement error models
Abstract
A multivariate ultrastructural measurement error model is considered and it is assumed that some prior information is available in the form of exact linear restrictions on regression coefficients. Using the prior information along with the additional knowledge of covariance matrix of measurement errors associated with explanatory vector and reliability matrix, we have proposed three methodologies to construct the consistent estimators which also satisfy the given linear restrictions. Asymptotic distribution of these estimators is derived when measurement errors and random error component are not necessarily normally distributed. Dominance conditions for the superiority of one estimator over the other under the criterion of Lowner ordering are obtained for each case of the additional information. Some conditions are also proposed under which the use of a particular type of information will give a more efficient estimator.
Year
DOI
Venue
2009
10.1016/j.jmva.2008.12.014
J. Multivariate Analysis
Keywords
Field
DocType
multivariate ultrastructural measurement error,linear restriction,reliability matrix,exact linear restriction,löwner ordering,measurement errors,measurement error,efficient estimator,62h12,consistent estimation,62j05,additional knowledge,consistent estimator,covariance matrix,measurement error model,additional information,ultrastructural model,regression coefficient,prior information,normal distribution,satisfiability,asymptotic distribution
Econometrics,Efficient estimator,Errors-in-variables models,Covariance matrix,Statistics,Prior probability,Mathematics,Linear regression,Asymptotic distribution,Consistent estimator,Estimator
Journal
Volume
Issue
ISSN
100
7
Journal of Multivariate Analysis
Citations 
PageRank 
References 
5
0.86
2
Authors
3
Name
Order
Citations
PageRank
Shalabh1184.96
Gaurav Garg223220.61
Neeraj Misra3225.51