Parameter estimators of nonlinear quantile regression

A. V. Ivanov, I. V. Orlovsky

National Technical of University of Ukraine ``KPI'', Peremogy Ave., Kyiv, Ukraine

Abstract: We have obtained the asymptotic normality of parameter estimators of a nonlinear quantile regression with nonsymmetric random noise. Introduction Here, we examine the asymptotic normality of Koenker and Basset estimators [1] or the generalized least moduli estimators (GLME) of nonlinear regression model parameters that generalize least moduli estimators for non-symmetric observation errors. The consistency property of GLME has been considered in [2].

Keywords: Nonlinear quantile regression, parameter estimator.

MSC: Primary 62J02; Secondary 62J99

Language: English

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