Affiliations: [a] Department of Statistics, Institute of Statistical Information, Inje University, Kimhae, Korea | [b] Department of Applied Statistics, Dankook University, Gyeonggido, Korea
Correspondence:
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Corresponding author: Changha Hwang, Department of Applied Statistics, Dankook University, Gyeonggido 448-160, Korea. E-mail: [email protected].
Abstract: Quantile regression models with errors in variables have received a great deal of attention in the social and natural sciences. Some efforts have been devoted to develop effective estimation methods for such quantile regression models. In this paper we propose a kernel-based orthogonal quantile regression model that effectively considers the errors on both input and response variables. We also provide a generalized cross validation method for choosing the hyperparameters and the ratios of the error variances which affect the performance of the proposed models. The proposed method is evaluated through simulations.
Keywords: Errors-in-variables, generalized cross validation, kernel, measurement error, orthogonal residual, quantile regression, support vector machine, support vector quantile regression
