Add to ORKGThis paper considers the estimation of the coefficient vector in a linear regression model subject to a set of stochastic linear restrictions binding the regression coefficients, and presents the method of weighted mixed regression estimation which permits to assign possibly unequal weights to the prior information in relation to the sample information. Efficiency properties of this estimation procedure are analyzed when disturbances are not necessarily normally distributed. (orig.)Available from TIB Hannover: RR 6137(122) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman
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This paper considers the estimation of the coefficient vector in a linear regression model subject t...
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The present paper considers the weighted mixed regression estimation of the coefficient vector in a ...
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This paper considers the estimation of the coefficient vector in a linear regression model subject t...
The use of prior information in linear regression analysis is well known to provide more efficient e...
The present paper considers the weighted mixed regression estimation of the coefficient vector in a ...
This paper deals with the application of the weighted mixed regression estimation of the coefficient...
A new relative efficiency in linear model in reference is instructed into the linear weighted regres...
This paper considers the estimation of coefficients in a linear regression model with missing observ...
We introduce the weighted mixed almost unbiased ridge estimator (WMAURE) based on the weighted mixed...
We present a novel method for the estimation of variance parameters in generalised linear mixed mode...
This paper investigates an one-step procedure for the General Least Squares Estimation (GLSE) in the...
Abstract This paper considers a linear regression model with stochastic restrictions,we propose a ne...
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This article is concerned with the predictions in linear mixed models under stochastic linear restri...
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