Efang Kong

Uniform Bahadur representation of the backfitting estimator for additive quantile models and its applications

Efang Kong, Lan Wang, Yichao Wu

This work is concerned with the Backfitting (BF) algorithm for the nonparametric additive quantile regression (AQR) model. We establish a strong uniform consistency rate for the Bahadur representation of the BF estimators, which includes the two stage estimator of [11] as a …

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Direction estimation for partial linear single index models with discrete covariates

Wushuang Tan, Haoran Zhan, Efang Kong

Compared to classical linear regression model, partial linear single index model (PLSIM), is better in accommodating non-linear effects while mitigating the so called curse of dimensionality. Most existing methods allow continuous covariates to be included in both the linear and nonlinear components, …

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Gradient-induced variable selection in reproducing kernel Hilbert space for survival analysis

Xin Tong Tan, Mei Yan, Efang Kong

The analysis of survival data is often hampered by a potentially very large number of covariates compounded with incomplete data. This paper considers the problem of variable selection where the response is subject to random (right) censoring. We introduce a model-free variable …

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Power Boosting: Fusion of Multiple Test Statistics via Resampling

Efang Kong, Yü Liu, Yingcun Xia

For the same null hypothesis, there usually exist multiple valid test statistics.In nearly all cases, any individual statistic is only powerful against specific types of alternatives, and could be rather weak in picking up signals of other types.It is thus crucial, especially …

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Choosing shape parameters for regression in reproducing kernel Hilbert space and variable selection

Xin Tong Tan, Yingcun Xia, Efang Kong

The Gaussian radial basis function (RBF) is a widely used kernel function in kernel-based methods. The parameter in RBF, referred to as the shape parameter, plays an essential role in model fitting. In this paper, we propose a method to select the …

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Bootstrap Tests for High-Dimensional White-Noise

Lengyang Wang, Efang Kong, Yingcun Xia

The testing of white-noise (WN) is an essential step in time series analysis. In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. We propose an easy-to-implement bootstrap method for high-dimensional …

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A Permutation Test for Two-Sample Means and Signal Identification of High-dimensional Data

Efang Kong, Lengyang Wang, Yingcun Xia, Jin Liu

Quantile based dimension reduction in censored regression

Mei Yan, Efang Kong, Yingcun Xia

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Factor models for asset returns based on transformed factors

Jialiang Li, Wenyang Zhang, Efang Kong

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Composite Coefficient of Determination and Its Application in Ultrahigh Dimensional Variable Screening

Efang Kong, Yingcun Xia, Wei Zhong

In this article, we propose to measure the dependence between two random variables through a composite coefficient of determination (CCD) of a set of nonparametric regressions. These regressions take consecutive binarizations of one variable as the response and the other variable as …

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Global Bahadur representation for nonparametric censored regression quantiles and its applications

Efang Kong, Oliver B. Linton, Yingcun Xia

This paper is concerned with the nonparametric estimation of regression quantiles where the response variable is randomly censored. Using results on the strong uniform convergence of U-processes, we derive a global Bahadur representation for the weighted local polynomial estimators, which is sufficiently …

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On the Efficiency of Online Approach to Nonparametric Smoothing of Big Data

Efang Kong, Yingcun Xia