Runze Li, Ph.D.

Recent Publications

Yang, G., Yang, S., & Li, R. (in press). Feature screening in ultrahigh dimensional generalized varying-coefficient models. Statistica Sinica.
Shi, C., Song, R., Chen, Z., & Li, R. (in press). Linear hypothesis testing for high dimensional generalized linear models. Annals Of Statistics.
Kurum, E., Hughes, J., Li, R., & Shiffman, S. (2018). Time-varying copula models for longitudinal data. Statistics And Its Interface, 11(2), 203–221. http://doi.org/10.4310/SII.2018.v11.n2.a1
Zhu, L., Xu, K., Li, R., & Zhong, W. (2017). Project correlation between two random vectors. Biometrika, 104(4), 829-843. http://doi.org/10.1093/biomet/asx043
Liu, H., Yao, T., Li, R., & Ye, Y. (2017). Folded concave penalized sparse linear regression: complexity, sparsity, statistical performance, and algorithm theory for local solutions. Mathematical Programming, Series A, 166(1-2), 207-240. http://doi.org/10.1007/s10107-017-1114-y
Miao, J., Chen, Z., Sebastian, A., Wang, Z., Shrestha, S., Li, X., et al. (2017). Sex-specific biology of the human malaria parasite revealed from transcriptomes and proteomes of male and female genotypes. Molecular And Cellular Proteomics, 16(4), 537-551. http://doi.org/10.1074/mcp.M116.061804
Wang, L., Liu, J., Li, Y., & Li, R. (2017). Model-free conditional independence feature screening for ultrahigh dimensional data. Science China Mathematics, 60(3), 551-568. http://doi.org/10.1007/s11425-016-0186-8
Li, R., Liu, J., & Lou, L. (2017). Variable selection via partial correlation. Statistica Sinica, 27, 983-996. http://doi.org/10.5705/ss.202015.0473
Li, R., Ren, J. J., Yang, G., & Ye, Y. (2017). Asymptotic behavior of Cox's partial likelihood and its application to variable selection. Statistica Sinica. http://doi.org/10.5705/ss.202016.0401
Ma, S., Li, R., & Tsai, C. - L. (2017). Variable screening via quantile partial correlation. Journal Of The American Statistical Association, 112, 650-663. http://doi.org/10.1080/01621459.2016.1156545
Zhang, X., Wu, Y., Wang, L., & Li, R. (2016). Variable Selection for Support Vector Machines in Moderately High Dimensions. Journal Of The Royal Statistical Society, Series B, 78(1), 53-76. http://doi.org/10.1111/rssb.12100
Liu, H., Yao, T., & Li, R. (2016). Global solutions to folded concave penalized nonconvex learning. Annals Of Statistics, 44, 629-659. http://doi.org/10.1214/15-AOS1380
Yang, S., Cranford, J. A., Li, R., Zucker, R. A., & Buu, A. (2015). A time-varying effect model for studying gender differences in health behavior. Statistical Methods In Medical Research.
JingYuan, L., Wei, Z., & Li, R. (2015). A selective overview of feature screening for ultrahigh-dimensional data. Sci China Math, 58(10), 2033-2054. http://doi.org/10.1007/s11425-015-5062-9
Chen, Z., Li, R., & Li, Y. (2015). Varying coefficient models for data with auto-correlated error process. Statistica Sinica. http://doi.org/10.5705/ss.2012.301
Yang, H., Cranford, J. A., Li, R., & Buu, A. (2015). Two-stage model for time-varying effects of discrete longitudinal covariates with applications in analysis of daily process data. Stat Med, 34(4), 571-81. http://doi.org/10.1002/sim.6368
Selya, A., Updegrove, N., Rose, J. S., Dierker, L. D., Tan, X., Hedeker, D., et al. (2015). Nicotine-dependence-varying effects of smoking events on momentary mood changes among adolescents. Addictive Behaviors, 41, 65-71. http://doi.org/10.1016/j.addbeh.2014.09.028
Dziak, J. J., Li, R., Tan, X., Shiffman, S., & Shiyko, M. P. (2015). Modeling Intensive Longitudinal Data With Mixtures of Nonparametric Trajectories and Time-Varying Effects. Psychol Methods. http://doi.org/10.1037/met0000048
Cui, H., Li, R., & Zhong, W. (2015). Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis. J Am Stat Assoc, 110(510), 630-641. http://doi.org/10.1080/01621459.2014.920256
Wang, L., Peng, B., & Li, R. (2015). A high-dimensional nonparametric multivariate test for mean vector. Journal Of The American Statistical Association. http://doi.org/10.1080/01621459.2014.988215