Research on High-Dimensional Data Analysis

Variable screening for high-dimensional data

Pan, R., Wang, H. and Li, R. (in press). On the ultrahigh dimensional linear discriminant analysis problem with a diverging number of classes. Journal of the American Statistical Association.

Zhong, W., Zhu, L., Li, R. & Cui, H. (in press). Regularized quantile regression and robust feature screening for single index models. Statistica Sinica.

Li, J., Zhong, W., Li, R., & Wu, R. (2014). A fast algorithm for selecting gene-gene interactions in genome-wide association studies. Annals of Applied Statistics.

Cui, H., Li, R., & Zhong, W. (2014). Model-free feature screening for ultrahigh dimensional discriminant Analysis. Journal of the American Statistical Association. Advance online publication. doi: 10.1080/01621459.2014.920256

Liu, J., Li, R., & Wu, R. (2014). Feature selection for varying coefficient models with ultrahigh-dimensional covariates. Journal of the American Statistical Association, 109, 266-274.

Huang, D., Li, R., & Wang, H. (2014). Feature screening for ultrahigh dimensional categorical data with applications. Journal of Business and Economic Statistics, 32, 237-244.

Wang, L., Kim. Y., & Li, R. (2013). Calibrating nonconvex penalized regression in ultrahigh dimension. Annals of Statistics, 41, 2505-2536.

Fan, Y., & Li, R. (2012). Variable selection in linear mixed effects models. Annals of Statistics, 40, 2043 – 2068. PMC Journal- In Process

Li, R., Zhong, W., & Zhu, L. (2012). Feature screening via distance correlation learning. Journal of the American Statistical Association. 107, 1129-1139.

Wang, L., Wu, Y., & Li, R. (2012). Quantile regression for analyzing heterogeneity in ultra-high dimension. Journal of the American Statistical Association, 107, 204-222. PMCID: PMC3471246

Zhu, L, Li, L., Li, R., & Zhu, L.-X. (2011). Model-free feature screening for ultrahigh dimensional data. Journal of the American Statistical Association, 106, 1464–1475.

Variable selection for independent and identically distributed data

Zhong, W., Zhu, L., Li, R. & Cui, H. (in press). Regularized quantile regression and robust feature screening for single index models. Statistica Sinica.

Li, J., Wang, Z., Li, R., & Wu, R. (2015). Bayesian group LASSO for nonparametric varying-coefficient models with application to functional genome-wide association studies. Annals of Applied Statistics.

Zhang, X., Wu, Y., Wang, L., & Li, R. (2015). Variable selection for support vector machines in moderately high dimensions. Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Wang, L., Kim, Y., & LI, R. (2013). Calibrating nonconvex penalized regression in ultrahigh dimension. Annals of Statistics, 41, 2505-2536.

Zhang, Y., Li, R., & Tsai, C.-L. (2010). Regularization parameter selections via generalized information criterion. Journal of the American Statistical Association, 105,312-323.

Kai, B., Li, R., & Zou, H. (2010). New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models. Annals of Statistics, 39, 305-332. PMCID: PMC3109949

Liang, H, Liu, X., Li, R., & Tsai, C.-L. (2010). Estimation and testing for partially linear single-index models. Annals of Statistics, 38, 3811-3836.

Wang, L., & Li, R. (2009). Weighted Wilcoxon-type smoothly clipped absolute deviation method. Biometrics, 65, 564-571.

Li, R., & Liang, H., (2008). Variable selection in semiparametric regression modeling. Annals of Statistics, 36, 261-286.

Zou, H., & Li, R., (2008). One-step sparse estimates in nonconcave penalized likelihood models (with discussion). Annals of Statistics, 26, 1509-1566.

Li, R. (2008). Discussion of “Sure independence screening for ultrahigh dimensional feature space” by Fan and Lv. Journal of the Royal Statistical Society, Series B, 70, 898.

Wang, H., Li, R., & Tsai, C.L., (2007). Tuning parameter selectors for the smoothly clipped absolute deviation method. Biometrika, 94, 553-568.

Hunter, D., & Li, R., (2005). Variable selection using MM algorithms. Annals of Statistics, 33, 1617-1642.

Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96, 1348-1360.

Variable selection for longitudinal data

Dziak, J., Li, R. & Qu, A. (2008). An overview on quadratic inference function approaches for longitudinal data. In J. Fan, X. Lin & J. Liu, (Eds.), New developments in biostatistics and bioinformatics (pp. 49-72). Singapore: World Scientific Publishing and Beijing: Higher Education Press.

Dziak, J., & Li, R. (2007). An overview on variable selection for longitudinal data. In D. Hong & Y. Shyr (Eds.) Quantitative Medical Data Analysis using Mathematical Tools and Statistical Techniques. (pp. 3-24). Singapore: World Sciences Publishers.

Fan, J., & Li, R. (2004). New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. Journal of the American Statistical Association, 99, 710-723.

Variable selection for survival data

Cai, J., Fan, J., Li, R. & Zhou, H. (2005). Variable selection for multivariate failure time data. Biometrika, 92, 303-316.

Fan, J., Li, G. & Li, R.(2005). An overview on variable selection for survival data analysis. In J. Fan & G. Li, (Eds.) Contemporary multivariate analysis and experimental designs (pp. 315-336). Singapore: World Sciences Publishers.

Fan, J., & Li, R. (2002). Variable selection for Cox’s proportional hazard model and frailty model. Annals of Statistics, 30, 74-99.

Variable selection for measurement error data

Ma, Y., & Li, R. (2010) Variable selection in measurement error models. Bernoulli, 16, 274-300.

Liang, H., & Li, R., (2009). Variable selection for partially linear models with measurement Errors. Journal of the American Statistical Association, 104, 234-248.

Variable selection for screening experiments

Li, R., & Lin, D.K.-J. (2008). Variable selection for screening experiments. Quality Technology and Quantitative Management, 6, 271-280.

Li, R., & Lin, D.K.-J. (2002). Data analysis in supersatured designs. Statistics and Probability Letters, 59, 135-144.

Applications of variable selection (substance abuse, psychiatrics and genetics)

Buu, A. Johnson, N.J., Li, R., & Tan, X. (2011). New variable selection methods for zero-inflated count data with applications to the substance abuse field. Statistics in Medicine, 30, 2326-2340. PMCID: PMC3133860

Wang, Y., Chen, H., Li, R., Duan, N., & Lewis-Fernandez, R. (2011). Prediction-based structured variable selection through receiver operating curve. Biometrics, 67,896-905.

Li, J., Das, K., Fu, G., Li, R., & Wu, R. (2011). The Bayesian LASSO for genome-wide association studies. Bioinformatics, 27, 516-523.