Finite Sample Properties of a Multivariate Extension of Double Regression

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This paper provides a study of a multivariate generalization of a method known as double regression, using information from concomitant variables in a double-sampling scheme in order to increase efficiency relative to the ordinary least squares estimator of the linear regression coefficients. Attention is given to the small-sample properties of the estimators in a biometrical context, and some improvements are made on former results concerning, among others, estimation of the residual variance. Emphasis is also made on the interest of the method on experimental costs reduction through an example in the context of carcass dissection studies.