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.
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