@article{kong_bondell_wu_2018, title={FULLY EFFICIENT ROBUST ESTIMATION, OUTLIER DETECTION AND VARIABLE SELECTION VIA PENALIZED REGRESSION}, volume={28}, ISSN={["1996-8507"]}, DOI={10.5705/ss.202016.0441}, abstractNote={: This paper studies the outlier detection and variable selection problem in linear regression. A mean shift parameter is added to the linear model to reflect the effect of outliers, where an outlier has a nonzero shift parameter. We then apply an adaptive regularization to these shift parameters to shrink most of them to zero. Those observations with nonzero mean shift parameter estimates are regarded as outliers. An L1 penalty is added to the regression parameters to select important predictors. We propose an efficient algorithm to solve this jointly penalized optimization problem and use the extended Bayesian information criteria tuning method to select the regularization parameters, since the number of parameters exceeds the sample size. Theoretical results are provided in terms of high breakdown point, full efficiency, as well as outlier detection consistency. We illustrate our method with simulations and data. Our method is extended to high-dimensional problems with dimension much larger than the sample size.}, number={2}, journal={STATISTICA SINICA}, author={Kong, Dehan and Bondell, Howard D. and Wu, Yichao}, year={2018}, month={Apr}, pages={1031–1052} }
 @misc{kong_maity_hsu_tzeng_2018, title={Rejoinder to "A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine"}, volume={74}, ISSN={["1541-0420"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85032786553&partnerID=MN8TOARS}, DOI={10.1111/biom.12786}, abstractNote={Dehan Kong , Arnab Maity, Fang-Chi Hsu, and Jung-Ying Tzeng Department of Statistical Sciences, University of Toronto, Ontario, Canada Department of Statistics, North Carolina State University, North Carolina, U.S.A. Department of Biostatistical Sciences, Wake Forest University, North Carolina, U.S.A. Department of Statistics and Bioinformatics Research Center North Carolina State University, North Carolina, U.S.A. Department of Statistics, National Cheng-Kung University, Taiwan ∗email: kongdehan@utstat.toronto.edu}, number={2}, journal={BIOMETRICS}, author={Kong, Dehan and Maity, Arnab and Hsu, Fang-Chi and Tzeng, Jung-Ying}, year={2018}, month={Jun}, pages={767–768} }
 @article{kong_bondell_wu_2015, title={Domain selection for the varying coefficient model via local polynomial regression}, volume={83}, ISSN={["1872-7352"]}, DOI={10.1016/j.csda.2014.10.004}, abstractNote={In this article, we consider the varying coefficient model, which allows the relationship between the predictors and response to vary across the domain of interest, such as time. In applications, it is possible that certain predictors only affect the response in particular regions and not everywhere. This corresponds to identifying the domain where the varying coefficient is nonzero. Towards this goal, local polynomial smoothing and penalized regression are incorporated into one framework. Asymptotic properties of our penalized estimators are provided. Specifically, the estimators enjoy the oracle properties in the sense that they have the same bias and asymptotic variance as the local polynomial estimators as if the sparsity is known as a priori. The choice of appropriate bandwidth and computational algorithms are discussed. The proposed method is examined via simulations and a real data example.}, journal={COMPUTATIONAL STATISTICS & DATA ANALYSIS}, author={Kong, Dehan and Bondell, Howard D. and Wu, Yichao}, year={2015}, month={Mar}, pages={236–250} }