This paper considers the problem of nonparametric quantile regression under the
assumption that the target conditional quantile function is a composition of a sequence of …

In this paper, we study the properties of robust nonparametric estimation using deep neural
networks for regression models with heavy tailed error distributions. We establish the non …

In this paper, we develop a novel efficient and robust nonparametric regression estimator
under a framework of feedforward neural network. There are several interesting …

In this paper we propose and study a version of the Dyadic Classification and Regression
Trees (DCART) estimator from Donoho (1997) for (fixed design) quantile regression in …

This thesis develops and assesses new ways to study the conditional quantiles of a
population using a sample of data that represents the population. All methods presented …

This thesis explores several aspects of econometric methods in time series forecasting of
both macroeconomic and financial variables. The contribution is provided in three essays …

With the development of science and technology, various data with large size and high
dimensionality are collected, stored, and analyzed. Meanwhile, data science including …