M-quantile regression is defined as a ‘quantile-like’ generalization of robust regression based on influence functions. The paper outlines asymptotic properties for the M-quantile regression coefficients estimators in the case of i.i.d. data with stochastic regressors, paying attention to ad- justments due to the first-step scale estimation. A variance estimator of the M-quantile regression coefficients based on the sandwich approach is proposed. Empirical results show that this estima- tor appears to perform well under different simulated scenarios. The sandwich estimator is applied in the small area estimation context for the estimation of the mean squared error of an estimator for the small area means. The results obtained improve previous findings, especially in the case of heteroskedastic data.

ASYMPTOTIC PROPERTIES AND VARIANCE ESTIMATORS OF THE M-QUANTILE REGRESSION COEFFICIENTS ESTIMATORS

Salvati, Nicola
2015

Abstract

M-quantile regression is defined as a ‘quantile-like’ generalization of robust regression based on influence functions. The paper outlines asymptotic properties for the M-quantile regression coefficients estimators in the case of i.i.d. data with stochastic regressors, paying attention to ad- justments due to the first-step scale estimation. A variance estimator of the M-quantile regression coefficients based on the sandwich approach is proposed. Empirical results show that this estima- tor appears to perform well under different simulated scenarios. The sandwich estimator is applied in the small area estimation context for the estimation of the mean squared error of an estimator for the small area means. The results obtained improve previous findings, especially in the case of heteroskedastic data.
Bianchi, Anamaria; Salvati, Nicola
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/208067
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