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Summary of research interests
Dr. Li is interested in
the fields of variable selection, local modeling,
functional/intensive longitudinal data analysis, semiparametric regression, design of
experiment, and modeling computer experiments. His primary
research focuses on the topics of variable selection, local
modeling and modeling computer
experiments.
Variable
selection is fundamental to statistical modeling. 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. His work has
shown 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/intensive longitudinal data analysis.
Functional data are also called 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.
Representative publications
Li, R. and Nie, L. (2008). Efficient statistical inference procedures for
partially nonlinear models and their applications. Biometrics. In press. [pdf]
Zou, H. and Li, R.
(2008). One-step sparse estimates in nonconcave penalized likelihood models. Annals of Statistics. In press. [pdf]
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]
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]
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]
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]
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]
Last updated: 14 July
2008
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Professor of Statistics
