People of the Methodology Center - Runze Li, Ph.D.

Penn State Shield

The Methodology Center

Main Menu

Software Downloads

About the Center

User Login

Follow Us

Methodology Center Twitter Page

Methodology Center RSS Feeds

Methodology Center Facebook Page

People of the Methodology Center

Home People Runze Li, Ph.D.

Runze Li, Ph.D.
Primary Investigator, The Methodology Center
Professor, Statistics
Professor, Public Health Sciences


	The Methodology Center The Pennsylvania State University 204 E. Calder Way, Suite 400 State College, PA 16801

	This e-mail address is being protected from spambots. You need JavaScript enabled to view it
	814-863-9481 (Methodology Center)
	814-865-1555 (Statistics Office)
	814-863-0000

Information:

Education:
	Ph.D., University of North Carolina at Chapel Hill, 2000 (Statistics)
Teaching:
	Fall 2007: Stat 553: Asymptotic Tools
Research Interests:
	Li is interested in the fields of variable selection, local modeling, functional data analysis and designs of experiment. His primary research focuses on the topics of variable selection and local modeling. Variable selection is fundamental to statistical modeling. Many approaches in use are stepwise selection procedures, such as best subset variable selection and stepwise backward elimination, which can be expensive in computation and ignore stochastic errors in the variable selection process. In Li's works, new approaches are proposed to select significant variables for various statistical models. Based on penalized likelihood, the proposed approaches delete insignificant covariates by estimating their coefficients to be zero, and therefore simultaneously select significant variables and estimate parameters. It has shown in his works that the proposed approaches have oracle properties, namely, they work as well as if the correct submodel were known. Li is also interested in the topic of functional data analysis. Functional data is also called as curve data. In fact, longitudinal data, repeated measurements and growth curves are special cases thereof. In his work, local likelihood methodology was used to deal with efficient estimation for various nonparametric models. Further, nonparametric maximum likelihood ratio type of goodness of fit test is proposed for nonparametric regression models used in functional data analysis.
Select Publications:
	Liang, H., & Li, R., (2008). Variable selection for partially linear models with measurement Errors. Journal of American Statistical Association. To appear. Yin, J., Geng, Z., Li, R., & Wang, H., (2008). Nonparametric covariance model. Statistica Sinica. To appear. Wang, L., & Li, R., (2008). Weighted Wilcoxon-type smoothly clipped absolute deviation method. Biometrics. In Press. Li, R., & Nie, L., (2008). Efficient statistical inference procedures for partially nonlinear models and their applications. Biometrics. 64, 904-911. Zou, H., & Li, R., (2008). One-step sparse estimates in nonconcave penalized likelihood models (with discussion). Annals of Statistics. 36, 1509-1566. Li, R., & Liang, H., (2008). Variable selection in semiparametric regression modeling. Annals of Statistics. 36, 261-286. Wang, H., Li, R., & Tsai, C.-L., (2007). Tuning parameter selectors for the smoothly clipped absolute deviation method. Biometrika. 94, 553-568. Li, R., & 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., & Li, R., (2007). Analysis of longitudinal data with semiparametric estimation of covariance function. Journal of American Statistical Association. 102, 632-641. Fan, J., & 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. Qu, A., & Li, R., (2006). Nonparametric modeling and inference function for longitudinal data. Biometrics. 62, 379-391. Zhang, A., Fang, K.-T., Li, R., & Sudjianto, A., (2005). Majorization framework for fractional factorial designs. Annals of Statistics. 33, 2837-2853. Hunter, D., & Li, R., (2005). Variable selection using MM algorithms. Annals of Statistics. 33, 1617-1642. Cai, J., Fan, J., Li, R., & Zhou, H. (2005). Variable selection for multivariate failure time data. Biometrika. 92, 303-316. Li, R., & Sudjianto, A., (2005). Analysis of computer experiments using penalized likelihood in Gaussian kriging Models. Technometrics. 47, 111-120. Li, R., & Chow, M., (2005). Evaluation of reproducibility for paired functional data. Journal of Multivariate Analysis. 93, 81-101. Fan, J., & Li, R., (2004). New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. Journal of American Statistical Association, 99, 710-723. Fan, J., & Li, R., (2002). Variable Selection for Cox's Proportional Hazards Model and Frailty Model. Annals of Statistics. 30, 74-99. Fan, J., & Li, R., (2001). Variable selection via nonconcave penalized likelihood and it oracle properties, Journal of American Statistical Association. 96, 1348-1360. Liang, J., Fang, K.T., Hickernell, F., & Li, R., (2001). Testing multivariate uniformity and its applications. Mathematics of Computation. 70, 337-355. Cai, Z., Fan, J., & Li, R., (2000). Efficient estimation and inferences for varying coefficient models. Journal of the American Statistical Association. 95, 888-902.