Journal Papers
Luo, B. Gao, X.L. (2022) High-dimensional robust approximated M-estimators for mean regression with asymmetric data, Journal of Multivariate Analysis, https://doi.org/10.1016/j.jmva.2022.105080
Luo, B. Gao, X.L. and Halabi, S. (2022) Penalized Weighted Proportional Hazards Model for Robust Variable Selection and Outlier Detection, Statistics in Medicine, https://doi.org/10.1002/sim.9424 .
Peng, Y., Gao, X.L. and Luo, B. (2022). Robust Moderately Clipped LASSO for Simultaneous Outlier Detection and Variable Selection, Sankhya B, https://doi.org/10.1007/s13571-022-00279-0.
Luo, B. and Gao, X.L. (2021). A High-dimensional M-estimator Framework for Bi-level Variable Selection. Accepted by Annals of the Institute of Statistical Mathematics. https://arxiv.org/abs/1911.11646.
Rostandy, B. and Gao, X.L. (2019). Botanical metabolite ions extraction from full electrospray ionization mass spectrometry using high-dimensional penalized regression. Metabolomics 15: 136. https://doi.org/10.1007/s11306-019-1603-5
Tang, Y., He, J., Gao, X.L. , Yang, T. and Zeng, X. (2018). Continuous amperometric hydrogen gas sensing in ionic liquids, Analyst, 143, pp 4136–4146.
Gao, X.L. and Feng, Y. (2018). Penalize Weighted Least Absolute Deviation Regression, Statistics and Its Interface, Vol. 11, No. 1 (2018), pp. 79-89.
Bunch, R., Murray, C, Gao, X.L., and Hunt, L. (2017). Geographic Analysis of Domestic Violence Incident Locations and Neighborhood Level Influences, Accepted by International Journal of Applied Geospatial Research.
Gao, X.L., Ahmed, S.E. and Feng, Y. (2017). Rejoinder: Post Selection Shrinkage Estimation for High Dimensional Data Analysis. Applied Stochastic Models in Business and Industry,http://onlinelibrary.wiley.com/doi/10.1002/asmb.2245/full.
Gao, X.L., Ahmed, S.E. and Feng, Y. (2016). (Discussion paper) Post Selection Shrinkage Estimation for High Dimensional Data Analysis (DOI: http://onlinelibrary.wiley.com/doi/10.1002/asmb.2193/full), Applied Stochastic Models in Business and Industry.
Gao, X.L. (2016). A Flexible Shrinkage Operator for Fussy Grouped Variable Selection, Accepted by Statistical Papers . DOI:10.1007/s00362-016-0799-y
Gao, X.L. (2015) Penalized Weighted Low-rank Approximation for Robust Recovery of Recurrent Copy Number Variations, BMC Bioinformatics 2015, 16:407.
Gao, X.L. (2015). Asymptotic Properties of Fused Lasso Signal Approximator under L1 Loss, major revision invited.
Gao, X.L. and Luo, B. (2015). Robust High-dimensional Data Analysis Using a Weight Shrinkage Rule, submitted.
Gao, X.L. and Fang, Y. (2016). Penalized Weighted Least Squares for Simultaneous Outlier Detection and Robust Regression. https://arxiv.org/abs/1603.07427
Gao, X.L. and Ahmed, S.E. (2014). Efficient Adaptive Estimation Strategy in Partially Linear Regression Model with Diverging Number of Parameters, in Perspectives on Big Data Analysis, Contemporary Mathematics, Amer. Math. Soc.,622, 61-80.
Gillies, C. E., Gao, X.L. , Patel, N.V., Siadat, M.R., Wilson, G.D.(2012). Improved Feature Selection by Incorporating Gene Similarity into the LASSO, International Journal of Knowledge Discovery in Bioinformatics, 3(1), 1-13, DOI: 10.4018/jkdb.2012010101.
Wu, Y. and Gao, X.L. (2011).Sieve estimation with bivariate interval censored data, Journal of Statistics, Application and Theory, 5, 37-61.
Gao, X.L. and Fang, Y.X. (2011). A note on the generalized degrees of freedom under the L1 loss function. Journal of Statistical Planning and Inference, 141, 677-686.
Gao, X.L. and Huang, J. (2010) A Robust Penalized Method for the Analysis of Noisy DNA Copy Number Data. BMC Genomics, 11:517.
Gao, X.L. and Huang, J. (2010). Asymptotic analysis of high-dimensional LAD regression with Lasso. Statistica Sinica, 20, 1485-1506.
Jiang, X. Gao, X.L., Xu, W., Qian, X. and Sweeeney, L. (2008). Bias variance machine. CSAIL Student Workshop Paper.
Belzunce, F., Gao, X.L., Hu, T. and Pellerey, F (2004). Characterizations of the hazard rate order and IFR aging notion. Statistics & Probability Letters, 70, 235-242.
Deng, J., Gao,X.L. and Wang,C. "Using Bi-level Penalized Logistic Classifier to Detect Zombie Accounts in Online Social Networks," in Proc. of the 5th International Conference on Network, Communication and Computing (ICNCC'16), Kyoto, Japan, December 17-21, 2016, pp. 126-130, doi: 10.1145/3033288.3033349.
Zhao, X., Zhao,K., Gao, X.L. and Deng, J.. Leveraging Big Data Analytics to Investigate Online Sellers’ Pricing Strategies, Proceedings of the Workshop on e-Business(WeB), Dallas, TX, December 2015.