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Estimation of Regression Coeecients Subject to Exact Linear Restrictions When Some Observations Are Missing and Balanced Loss Function Is Used

This article considers a linear regression model when a set of exact linear restrictions binding the coeecients is available and some observations on the study variable are missing. Estimators for the vectors of regression coeecients are presented and their superiority properties with respect to the criteria of the variance covariance matrix and the risk under balanced loss functions are analyzed.