Moving from \citet{Rao1991} regression estimator, a class of biased estimators for the unknown mean of a survey variable is proposed when auxiliary information is available. The bias and the mean square error of the estimators belonging to the class are obtained to the first order of approximation and the expression for the optimum parameters minimizing the mean square error is given in a closed form. A simple condition allowing for outperforming the classical regression estimator is worked out. In order to investigate the performance of some estimators upon the regression one, a simulation study is carried out when some population parameters are supposed to be unknown.

An improved class of estimators for the population mean

PERRI, PIER FRANCESCO
2011-01-01

Abstract

Moving from \citet{Rao1991} regression estimator, a class of biased estimators for the unknown mean of a survey variable is proposed when auxiliary information is available. The bias and the mean square error of the estimators belonging to the class are obtained to the first order of approximation and the expression for the optimum parameters minimizing the mean square error is given in a closed form. A simple condition allowing for outperforming the classical regression estimator is worked out. In order to investigate the performance of some estimators upon the regression one, a simulation study is carried out when some population parameters are supposed to be unknown.
2011
Regression estimator; Rao estimator; Monte Carlo simulation; biased class; efficiency comparisons
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11770/125540
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