Welcome to Runze Li's Homepage

    

  

Runze Li, Verne M. Willaman Professor

    

Contact Information

Academic Positions & Education

Honors and Awards

Professional Service

Research Interests

Selected Publications

A. Book:

Fang, K.-T., Li, R. and Sudjianto, A. (2006). Design and Modeling for Computer Experiments. Chapman and Hall/CRC. Boca Raton, FL.

B. Selected Publications in Statistical Journals:

Ma, S., Li, R. and Tsai, C.-L. (2016). Variable screening via partial quantile correlation. Journal of American Statistical Association. Accepted.

Chen, Z., Fan, J. and Li, R. (2016). Error variance estimation in ultrahigh dimensional additive models. Journal of American Statistical Association. Accepted.

Li, R., Ren, J.J., Yang, G. and Ye, Y. (2016). Asymptotic behavior of Cox's partial likelihood and its application to variable selection. Statistica Sinica. Accepted.

Li, R., Liu, J. and Lou, L. (2016). Variable selection via partial correlation. Statistica Sinica. Accepted.

Liu, H., Yao, T. and Li, R. (2016). Global solutions to folded concave penalized nonconvex learning. Annals of Statistics, 44, 629 - 659. [pdf] [Supplement]

Pan, R., Wang, H. and Li, R. (2016). On the ultrahigh dimensional linear discriminant analysis problem with a diverging number of classes. Journal of American Statistical Association, 111, 169 -179. [pdf]

Zhang, X., Wu, Y., Wang, L. and Li, R. (2016). Variable selection for support vector machine in moderately high dimensions. Journal of Royal Statistical Society, Series B. 78, 53 - 76. [pdf]

Chu, W., Li, R. and Reimherr, M. (2016). Feature screening for time-varying coefficient models with ultrahigh dimensional longitudinal data. Annals of Applied Statistics, 10, 596 - 617. [pdf]

Li, D. and Li, R. (2016). Local composite quantile regression smoothing for Harris recurrent Markov processes. Journal of Econometrics, 194, 44 - 56. [pdf]

Lan, W., Zhong, P., Li, R., Tsai, C.-L. and Wang, H. (2016). Single coefficient test in high dimensional linear models. Journal of Econometrics, 195, 154 - 168. [pdf]

Zhang, X., Wu, Y., Wang, L. and Li, R. (2016). A consistent information criterion for support vector machines in diverging model spaces. Journal of Machine Learning Research, 17, 1 -26. [pdf]

Zhong, W., Zhu, L., Li, R. and Cui, H. (2016). Regularized quantile regression and robust feature screening for single index models. Statistica Sinica. 26 , 69 - 95. [pdf]

Xu, C., Lin, S., Fang, J. and Li, R. (2016). Prediction-based termination rule for greedy learning with massive data. Statistica Sinica, 26, 841 - 860. [pdf] [supplement]

Yang, G., Yu, Y., Li, R. and Buu, A. (2016). Feature screening in ultrahigh dimensional Cox's model. Statistica Sinica, 26, 881 - 902. [pdf]

Kurum, E., Li, R., Shiffman, S. and Yao, W. (2016). Time-varying coefficient models for joint modeling binary and continuous outcomes in longitudinal data. Statistica Sinica, 26, 979 - 1000. [pdf]

Liu, X, Cui, Y. and Li, R. (2016). Partial linear varying multi-index coefficient model for integrative gene-environment interactions. Statistica Sinica, 26, 1037 - 1060. [pdf]

Xu, C., Zhang, Y., Li, R. and Wu, X. (2016). On the Feasibility of Distributed Kernel Regression for Big Data. IEEE Transactions on Knowledge and Data Engineering, 28, 3041 - 3052. [pdf]

Cui, H., Li, R. and Zhong, W. (2015). Model-free feature screening for ultrahigh dimensional discriminant analysis. Journal of American Statistical Association. 110, 630 - 641. [pdf]

Wang, L., Peng, B. and Li, R. (2015). A high-dimensional nonparametric multivariate test for mean vector. Journal of American Statistical Association. 110, 1658 - 1669. [pdf]

Chen, Z., Li, R. and Li, Y. (2015). Varying-coefficient models for data with auto-correlated error process. Statistica Sinica. 25, 709 - 724. [pdf] and supplement [pdf]

Li, J., Wang, Z., Li, R. and Wu, R. (2015). Bayesian group LASSO for nonparametric varying-coefficient models with application to functional genome-wide association studies. Annals of Applied Statistics. 9, 640 - 664. [pdf]

Liu, J, Zhong, W. and Li, R. (2015). A selective overview of feature screening for ultrahigh dimensional data. Science China: Mathematics. 58, 2033 - 2054. [pdf]

Li, J., Zhong, W., Li, R. and Wu, R. (2014). A fast algorithm for detecting gene-gene interactions in genome-wide association studies. The Annals of Applied Statistics. 8, 2292 - 2318. [pdf]

Liu, J., Li, R. and Wu, R. (2014). Feature Selection for varying coefficient models with ultrahigh dimensional covariates. Journal of American Statistical Association. 109, 266 - 274. [pdf]

Chen, H., Wang, Y., Li, R. and Shear, K. (2014). A note on nonparametric regression test through penalized splines. Statistica Sinica. 24, 1143-1160. [pdf]

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

Huang, M, Li, R., Wang, H. and Yao, W. (2014). Estimating mixture of Gaussian processes by kernel smoothing. Journal of Business and Economic Statistics. 32, 259-270. [pdf]

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

Huang, M., Li, R. and Wang, S. (2013). Nonparametric mixture of regression models. Journal of American Statistical Association. 108, 929 - 941. [pdf]

Yao, W. and Li, R. (2013). New local estimation procedure for nonparametric regression function of longitudinal data. Journal of Royal Statistical Society, Series B. 75, 123-138. [pdf]

Zhu, L., Dong, Y. and Li, R. (2013). Semiparametric estimation of conditional heteroscedasticity through single index modeling. Statistica Sinica. 24, 1235 - 1256. [pdf]

Zhu, H., Li, R. and Kong, L. (2012). Multivariate varying coefficient models for functional responses. Annals of Statistics. 40, 2634 - 2666. [pdf]

Fan, Y. and Li, R. (2012). Variable selection in linear mixed effects models. Annals of Statistics. 40, 2043 - 2068. [pdf]

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

Wang, L., Wu, Y. and Li, R. (2012). Quantile regression for analyzing heterogeneity in ultrahigh dimension. Journal of American Statistical Association. 107, 214 - 222. [pdf]

Zhu, L, Li, L., Li, R. and Zhu, L.-X. (2011). Model-free feature screening for ultrahigh dimensional data. Journal of American Statistical Association. 106, 1464 - 1475. [pdf]

Kai, B., Li, R. and Zou, H. (2011). New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models. Annals of Statistics. 39, 305-332. [pdf]

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

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

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

Kai, B., Li, R. and Zou, H. (2010). Local CQR smoothing: an efficient and safe alternative to local polynomial regression. Journal of Royal Statistical Society, Series B. 72, 49-69. [pdf]

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

Yin, J., Geng, Z., Li, R. and Wang, H. (2010). Nonparametric covariance model. Statistica Sinica, 20, 469-479 [pdf] and supplement [pdf]

Wang, L., Kai, B. and Li, R. (2009). Local rank inference for varying coefficient models. Journal of American Statistical Association, 104, 1631-1645. [pdf]

Wang, L. and Li, R. (2009). Weighted Wilcoxon-type smoothly clipped absolute deviation method. Biometrics. 65, 564-571. [pdf] and Web Document [pdf]

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

Li, R. and Nie, L. (2008). Efficient statistical inference procedures for partially nonlinear models and their applications. Biometrics, 64, 904-911. [pdf] Web Document [pdf]

Zou, H. and Li, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models (with discussion). Annals of Statistics, 36, 1509-1566. [pdf] [Rejoinder]

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

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

Li, R. and Nie, L. (2007). A new estimation procedure for a partially nonlinear model via a mixed-effects approach. The Canadian Journal of Statistics, 35, 399-411.

Fan, J., Huang, T. and Li, R. (2007). Analysis of longitudinal data with semiparametric estimation of covariance function. Journal of American Statistical Association. 102, 632-641. [pdf]

Fan, J. and Li, R. (2006). Statistical Challenges with High Dimensionality: Feature Selection in Knowledge Discovery. Proceedings of the International Congress of Mathematicians (M. Sanz-Sole, J. Soria, J.L. Varona, J. Verdera, eds.), Vol. III, European Mathematical Society, Zurich, 595-622. [pdf]

Qu, A. and Li, R. (2006). Nonparametric modeling and inference function for longitudinal data. Biometrics. 62, 379-391 [pdf]

Zhang, A., Fang, K.-T., Li, R. and Sudjianto, A. (2005). Majorization framework for fractional factorial designs. Annals of Statistics. 33, 2837-2853. [pdf]

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

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

Li, R. and Sudjianto, A. (2005). Analysis of computer experiments using penalized likelihood in Gaussian kriging Models. Technometrics. 47, 111-120. [pdf]

Li, R. and Chow, M. (2005). Evaluation of reproducibility for paired functional data. Journal of Multivariate Analysis. 93, 81-101. [pdf]

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

Fan, J. and Li, R. (2002). Variable Selection for Cox's Proportional Hazards Model and Frailty Model. Annals of Statistics. 30, 74-99. [pdf]

Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and it oracle properties, Journal of American Statistical Association. 96, 1348-1360. [pdf]

Liang, J., Fang, K.T., Hickernell, F. and Li, R. (2001). Testing multivariate uniformity and its applications. Mathematics of Computation. 70, 337-355. [pdf]

Cai, Z., Fan, J. and Li, R. (2000). Efficient estimation and inferences for varying coefficient models. Journal of the American Statistical Association. 5, 888-902. [pdf]

C. Selected Interdisciplinary Research Works:

C1. Social Science Research

Yang, S., Cranford, J. A., Jester, J. M., Li, R., Zucker, R. A. and Buu, A. (2016). A time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes with applications in substance abuse research. Statistics in Medicine. Accepted.

Yang, S., Cranford, J. A., Li, R., Zucker, R. A. and Buu, A. (2016). Time-varying effect model for studying gender differences in health behavior. Statistical Methods in Medical Research. Accepted.

Yang, H., Li, R., Zucker, R and Buu, A. (2016). Two-stage model for time-varying effects of zero-inflated count longitudinal covariates with applications in health behavior research. Journal of Royal Statistical Society, Series C, 65, 431 - 444.

Dziak, J., Li, R., Tan, X., Shiffman, S. and Shiyko, M. (2015). Modeling intensive longitudinal data on smoking cessation with mixtures of nonparametric trajectories and time-varying effects. Psychological Methods. 20, 444 - 469.

Selya, A. S., Updegrove, N., Rose, J., Dierker, L., Tan, X., Hedeker, D., Li, R. and Mermelstein, R. J. (2015). Nicotine Dependence-Varying Effects of Smoking Events on Momentary Mood Changes among Adolescents. Addictive Behaviors. 41, 65-71.

Yang, H., Cranford, J., Li, R. and Buu, A. (2015). Two-stage model for time-varying effects of discrete longitudinal covariates with applications in analysis of daily process data. Statistics in Medicine. 34, 571 - 581.

Shiyko, M.P., Burkhalter, J., Li., R., and Park, B. J. (2014). Modeling nonlinear time-dependent treatment effects: An application of the time-varying effects model (TVEM). Journal of Consulting and Clinical Psychology. 82, 760 - 772.

Dziak, J., Li, R., Zimmerman, M. and Buu, A. (2014). Time-varying effect model for ordinal responses with applications in substance abuse research. Statistics in Medicine. 33, 5126 - 5137.

Buu, A., Li, R., Walton, M., Yang, H., Zimmerman, M. A., Cunningham, R. M. (2014). Changes in substance use-related health risk behaviors on the timeline follow-back interview as a function of length of recall period. Substance Use and Misuse. 49, 1259 - 1269.

Trail, J. B., Collins, L. M., Rivera, D. F., Li, R, Piper, M. E., Baker, T. B. (2014). Functional Data Analysis for Dynamical System Identification of Behavioral Processes. Psychological Methods. 19, 175 - 187.

Shiyko, M., Naab, P., Shiffman, S. and Li, R. (2014). Modeling complexity of EMA data: time-varying lagged effects of negative affect on smoking urges for subgroups of nicotine addiction. Nicotine & Tobacco Research. 16S2, S144 - S150.

Vasilenko, S., Piper, M., Lanza, S.T. Liu, X., Yang, J., Li, R. (2014). Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies. Nicotine & Tobacco Research. 16S2, S135 - S143.

Lanza, S.T., Vasilenko, S., Liu, X., Piper, M. and Li, R. (2014). Advancing Understanding of the Dynamics of Smoking Cessation Using the Time-Varying Effect Model. Nicotine & Tobacco Research. 16S2, S127 - S134.

Liu, X., Li, R., Lanza, S.T., Vasilenko, S. and Piper, M. (2013). Understanding the role of cessation fatigue in the smoking cessation process. Drug and Alcohol Dependence. 133, 548 - 555.

Selya1, A.S., Dierker, L. C., Rose, J. S., Hedeker, D., Tan, X., Li, R., Mermelstein, R.J. (2013). Time-varying effects of smoking quantity and nicotine dependence on adolescent smoking regularity. Drug and Alcohol Dependence. 128, 230-237.

Buu, A., Li, R., Tan, X. and Zucker, R. A. (2012). Statistical models for longitudinal zero-inflated count data with applications to the substance abuse field. Statistics in Medicine. 31, 4074 - 4086.

Tan, X., Shiyko, M., Li, R., Li, Y. and Dierker, L. (2012). Intensive longitudinal data and model with varying effects. Psychological Methods. 17, 61 - 77.

Shiyko, M. P., Lanza, S. T., Tan, X., Li, R. and Shiffman, S. (2012). Using the time-varying effects model (TVEM) to examine dynamic associations between negative affect and self confidence on smoking urges: differences between successful quitters and relapsers. Prevention Science. 13 , 288 - 299.

Cole, P. M., Tan, P. Z., Hall, S. E., Zhang, Y., Crnic, K. A., Blair, C. B., and Li, R. (2011). Developmental changes in anger expression and attention focus during a delay: Learning to wait. Developmental Psychology, 47, 1078 - 1089. DOI: 10.1037/a0023813

Buu, A. Johnson, N.J., Li, R. and 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.

Tan, X., Dierker, L., Li, R., Rose, J., and The Tobacco Etiology Research Network(TERN). (2011). How spacing of data collection may impact estimates of substance use trajectories? Substance Use and Misuse. 46 , 758 - 768

Dierker, L., Rose, J., Tan, X., Li, R. and The Tobacco Etiology Research Network(TERN) (2010). Uncovering multiple pathways to substance use: A comparison of methods for identifying population subgroups. The Journal of Primary Prevention. 31, 333-348.

Collins, L. M., Dziak, J. J. and Li, R. (2009). Design of experiments with multiple independent variables: A resource management perspective on complete and reduced factorial designs. Psychological Methods, 14, 202-224.

C2. Statistical genetics and Bioinformatics

Chu, W., Li, R. and Reimherr, M. (2016). Feature screening for time-varying coefficient models with ultrahigh dimensional longitudinal data. Annals of Applied Statistics, 10, 596 - 617. [pdf]

Yang, G., Yu, Y., Li, R. and Buu, A. (2016). Feature screening in ultrahigh dimensional Cox's model. Statistica Sinica, 26, 881 - 902. [pdf]

Liu, X, Cui, Y. and Li, R. (2016). Partial linear varying multi-index coefficient model for integrative gene-environment interactions. Statistica Sinica, 26, 1037 - 1060. [pdf]

Wang, N.T., Gocik, K., Li, R., Lindsay, B. and Wu, R. (2016). A block mixture model to map eQTLs for gene clustering and networking. Sci Rep, 6: 21193.

Li, J., Wang, Z., Li, R. and Wu, R. (2015). Bayesian group LASSO for nonparametric varying-coefficient models with application to functional genome-wide association studies. Annals of Applied Statistics. 9, 640 - 664. [pdf]

Percival, C. J., Huang, Y., Jabs, E. W., Li, R. and Richtsmeier, J. T. (2014), Embryonic craniofacial bone volume and bone mineral density in Fgfr2+P253R and nonmutant mice. Developmental Dynamics. 243, 541 - 551.

Das, K., Li, R., Sengupta, S. and Wu, R. (2013). A Bayesian semiparametric model for bivariate sparse longitudinal data. Statistics in Medicine. 32, 3899 - 3910.

Das, K., Li, J., Fu, G., Wang, Z., Li, R. and Wu, R. (2013). Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data. Statistics in Medicine. 32, 509 - 523.

Wang, Y., Huang, C., Fang, Y., Yang, Q. and Li, R. (2012). Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing. Journal of Royal Statistical Society, Series C. 61, 1 - 24.

Das, K., Li, J., Wang, Z., Gu, G., Tong, C. Li, Y., Xu, M., Ahn, K., Mauger, D.T. Li, R., and Wu, R. (2011). A dynamic model for genome-wide association studies. Human Genetics. 129, 629-639.

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

Wang, Y., Xu, M., Wang, Z., Tao, M., Zhu, J., Li, R., Wang, L. Berceli, S.A. and Wu, R. (2011). How to cluster gene expression dynamics in response to environmental signals. Briefings in Bioinformatics. doi:10.1093/bib/bbr032 ,

Fu, G., Wang, Z., Li, J. Das, K., Li, R. and Wu, L. (2011). Integrating ordinary differential equations into functional mapping of biological rhythms. Journal of Biological Dynamics, 5, 84-101.

Fu, G., Berg, A. Das, K., Li, J., Li, R. and Wu, R. (2010). A statistical model for mapping morphological shape. Theoretical Biology and Medical Modelling, 7:28, doi:10.1186/1742-4682-7-28

C3. Environmental and Meteorological Research

Kurum, E., Li, R., Wang, Y. and Senturk, D. (2014). Nonlinear varying-coefficient models with application to a photosynthesis study. Journal of Agricultural, Biological, and Environmental Statistics, 19, 57 - 81.

Yi, C., Ricciuto, D., Li, R., et al. (2010). Climate control to terrestrial carbon exchange across biomes and continents. Environmental Research Letters. 5:034007, doi: 10.1088/1748-9326/5/3/034007 (This paper won the United Nations' World Meteorological Organization (WMO) 2012 Gerbier-Mumm International Award.)

Yi, C., Li, R., Bakwin, P. S., Desai, A., Ricciuto, D. M., Burns, S., Turnipseed, A. A., Munger, J.W., Wofsy, S. C., Wilson, K., Meyers, T. P., Anderson, D. E., and Monson, R. K. (2004). A nonparametric method for separating photosynthesis and respiration components in CO_2 flux measurements. Geophysical Research Letters. 31, L17107, doi:10.1029/2004GL020490

C4. Neural Science, Chemometrics and Computer Experiments

Liu, H., Du, G. Zhang, L. , Lewis, M., Wang, X., Yao, T., Li, R. and Huang, X. (2016). Folded concave penalized learning in identifying multimodal MRI marker for Parkinson's disease. Journal of Neuroscience Methods, 268, 1 - 6.

Zhu, H., Kong, L., Li, R., Styner, M., Gerig, G., Lin, W. and Gilmore, J. H. (2011). FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics. Neuroimage. 56, 1412 - 1425

Yin, H., Fang, K.-T., Li, R. and Liang, Y.-Z. (2007). Empirical Kriging models and their applications to QSAR. Journal of Chemometrics. 21, 43-52.

Peng, X.-L., Yin, H., Li, R. and Fang, K.-T. (2006). The application of kriging and empirical kriging based on the variables selected by SCAD. Analytica Chimica Acta, 578, 178-185.

Fang, K.-T., Li, R. and Sudjianto, A. (2006). Design and Modeling for Computer Experiments. Chapman and Hall/CRC. Boca Raton, FL.

Li, R. and Sudjianto, A. (2005). Analysis of computer experiments using penalized likelihood in Gaussian kriging Models. Technometrics. 47, 111-120.

D. Acknowledgement

My research has been supported by National Science Foundation and National Institute of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the grant agents. The pdf files posted in this website are merely for convenience. If you or your institute do not have copyright to access the papers, you should not download the papers from this homepage.