Quantile regression with multilayer perceptrons
نویسندگان
چکیده
We consider nonlinear quantile regression involving multilayer perceptrons (MLP). In this paper we investigate the asymptotic behavior of quantile regression in a general framework. First by allowing possibly non-identifiable regression models like MLP's with redundant hidden units, then by relaxing the conditions on the density of the noise. In this paper, we present an universal bound for the overfitting of such model under weak assumptions. The main application of this bound is to give a hint about determining the true architecture of the MLP quantile regression model. As an illustration, we use this theoretical result to propose and compare effective criteria to find the true architecture of such regression model.
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