Penn State Department of Statistics HomePage
 
 

   Runze Li

         Professor of Statistics

                     Office: 413 Thomas Building, Tel.: 814-865-1555,  Fax: 814-863-7114, Email
 

I received my Ph.D. in 2000 from the  Department of Statistics, University of North Carolina at Chapel Hill


Academic Positions:

Distinguished Professor, Penn State University, 2012 –

Full Professor, Penn State University, 2008 – 2012

Associate Professor, Penn State University, 2005-2008

Assistant Professor, Penn State University, 2000-2005

 

Honors and Awards

NSF Career Award, 2004

Fellow, Institute of Mathematical Statistics

Fellow, American Statistical Association

The United Nations' World Meteorological Organization Gerbier-Mumm International Award

for 2012 (Selection criterion for this award)

 

Editorial Service:

   Co-Editor of Annals of Statistics (2013-2015)

   Associated Editor of Annals of Statistics (2007 -  2012)

   Associated Editor of Journal of American Statistical Association (2006 - 2012)

   Associated Editor of Statistica Sinica (2005 -  2012)

 

Research Interests:

Variable selection for high-dimensional data

Feature screening for ultrahigh-dimensional data

Longitudinal and intensive longitudinal data analysis

Nonparametric regression modeling and local polynomial regression,

Semiparametric regression modeling,

Statistical genetics and bioinformatics

Statistical applications to engineering, meteorological research, neural science research &

social behavioral science research

 

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:

 

Wang, L., Kim, Y. and Li, R. (2013). Calibrating nonconvex penalized regression in ultrahigh dimension. Annals of Statistics. In press.

 

Chen, H., Wang, Y., Li, R. and Shear, K. (2013). A note on nonparametric regression test through penalized splines. Statistica Sinica. To appear.

 

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 Web Documment [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 Documment [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] and Web Documment [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 Association99, 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.  95, 888-902.   [pdf]

C. Selected Interdisciplinary Research Works:

C1. Social Science Research

 

Trail, J. B., Collins, L. M., Rivera, D. F., Li, R, Piper, M. E., Baker, T. B. (2013). Functional Data Analysis for Dynamical System Identification of Behavioral Processes. Psychological Methods. To appear.

 

Shiyko, M., Naab, P., Shiffman, S. and Li, R. (2013). Modeling complexity of EMA data: time-varying lagged effects of negative affect on smoking urges for subgroups of nicotine addiction. Nicotine & Tobacco Research. To appear.

 

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. To appear.

 

Lanza, S.T., Vasilenko, S., Liu, X., Piper, M. and Li, R. (2013). Advancing Understanding of the Dynamics of Smoking Cessation Using the Time-Varying Effect Model. Nicotine & Tobacco Research. To appear.

 

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

 

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

 

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

 

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

 

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.