Runze Li, Ph.D. | The Methodology Center

Runze Li, Ph.D.

Runze Li, Ph.D. Principal Investigator, The Methodology Center

Distinguished Professor, Statistics

Professor, Public Health Sciences

 

The Methodology Center

The Pennsylvania State University

400 Calder Square II

State College, PA 16801

 

814-863-9481

Website

 

 

Education

Ph.D., University of North Carolina at Chapel Hill, 2000 (Statistics)

 

 

Research Interests

My research is primarily focused on the fields of variable selection, local modeling; I am also interested in functional data analysis and experimental design.

 

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 can ignore stochastic errors in the variable selection process. In my work, 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. We have shown that the proposed approaches have oracle properties; namely, they work as well as if the correct submodel were known.

 

I am also interested in the topic of functional data analysis. Functional data are also called curve data; longitudinal data, repeated measurements and growth curves are special cases of functional data. In my work, local likelihood methods were used for efficient estimation of parameters for various nonparametric regression models used in functional data analysis. Further, generalized likelihood ratio tests are proposed for goodness-of-fit tests on these models.

 

 

Current Projects and Collaborators

I am working on several related projects that apply nonparametric and semiparametric statistical modeling techniques to the analysis of intensive longitudinal data including ecological momentary assessment data. My collaborators on these projects are Lisa Dieker (Wesleyan University), Xianming Tan (McGill University), Mariya Shiyko (Northeastern University), Anne Buu (University of Michigan), Linda M. Collins, Stephanie Lanza, John Dziak, and Jingyun (Michael) Yang.

Honors and Awards

  • Co-Editor, Annals of Statistics, 2013-2015
  • The United Nations' World Meteorological Organization Gerbier-Mumm International Award, 2012 (Selection criteria for this award)
  • Fellow, American Statistical Association, 2011
  • Fellow, Institute of Mathematical Statistics, 2009
  • NSF Career Award, 2004
  • Chair, Scientific Program Committee for the Institute of Mathematical Statistics (IMS)-China International Conference on Statistics and Probability 2013, Chengdu, China. 

  • Scientific Program Committee member, The Second Taihu Lake International Statistical Forum, July 2013, Soochow, P. R. China.

 

 

Selected Grants

Center for Prevention and Treatment Methodology

National Institute on Drug Abuse P50 (Renewal)

2010-2015;

Role: Principal Investigator

 

Do Access Barriers to Autism Care Persist Despite Autism Insurance Mandate?

National Institute on Mental Health

2012-2015; Role: Investigator (PI: Li Wang)

 

Advancing Tobacco Research by Integrating Systems Science and Mixture Models

National Cancer Institute R01

2012-2015; Role: Investigator (PI: Stephanie Lanza)

 

 

Selected Publications

Journal Articles

Chen, H., Wang, Y., Li, R., & Shear, K. (in press). A note on a nonparametric regression test through penalized splines. Statistics Sinica.

Huang, M., Li, R., Wang, H., & Yao, W. (in press). Estimating mixture of Gaussian processes by kernel smoothing. Journal of Business and Economic Statistics.

Kurum, E., Li, R., Wang, Y., & Senturk, D. (in press). Nonlinear varying coefficient model and its applications. Journal of Agricultural, Biological, and Environmental Statistics.

Liu, J., Li, R., & Wu, R. (in press). Feature selection for varying coefficient models with ultrahigh dimensional covariates. Journal of the American Statistical Association.

Shiyko, M., Burkhalter, J. E., Li, R., & Park, B. J. (in press). Time-varying effects of surgical treatment on momentary assessments of symptom burden in lung cancer patients post hospital discharge: An application of the time-varying effects model (TVEM). Journal of Consulting and Clinical Psychology.

Vasilenko, S. A., Piper, M. E., Lanza, S. T. Liu, X., Yang, J. & Li, R. (in press). Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies. Nicotine and Tobacco Research, 16, (Suppl. 2), 135-143. PMCID: PMC4056442

Zhao, Z., Zhang, Y., & Li, R. (2014). Non-parametric estimation under strong dependence. Journal of Time Series Analysis, 35, 4-15.

Das, K., Li, J., Fu, G.,Wang, Z., Li, R. & Wu, R. (2013). Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data. Statistics in Medicine, 32, 509 - 523. PMCID: PMC3770845

Das, K., Li, R., Sengupta, S., & Wu, R. (2013). A Bayesian semiparametric model for bivariate sparse longitudinal data. Statistics in Medicine, 32, 3899-3910.  doi: 10.1002/sim.5790 PMCID: PMC3740051

Huang, D., Li, R., & Wang, H. (2013). Feature screening for ultrahigh dimensional categorical data with applications. Journal of Business and Economic Statistics. Advance online publication. doi:10.1080/07350015.2013.863158 

Huang, M., Li, R. & Wang, S. (2013). Nonparametric mixture of regression models. Journal of the American Statistical Association, 108, 929-941. PMCID: PMC3865811

Lanza, S. T., Vasilenko, S., Liu, X., Li, R., & Piper, M. (2013). Advancing the understanding of craving during smoking cessation attempts: A demonstration of the time-varying effect model. Nicotine and Tobacco Research. Advance online publication. doi: 10.1093/ntr/ntt128 PMC Journal—In process

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

Li, R., Lin, D. K. J., & Li, B. (2013). Statistical inference in massive data sets. Applied Stochastic Models in Business and Industry. 29, 399 - 409.

Percival, C. J., Huang, Y., Jabs, E. W., Li, R., & Richtsmeier, J. T. (2013). Bone volume and bone mineral density during growth of normal and Fgfr2 +/P253R mice. Developmental Dynamics. Advance online publication. doi: 10.1002/dvdy.24095

Selya, 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. PMCID: PMC3538104

Shiyko, M. P., Burkhalter, J., Li., R., & Park, B. J. (2013). Time-varying effects of surgical treatment on momentary assessments of symptom burden in lung cancer patients post hospital discharge: An application of the time-varying effects model (TVEM). Journal of Consulting and Clinical Psychology. Advance online publication. doi: 10.1037/a0035267

Shiyko, M., Naab, P., Shiffman, S., & 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. Advance online publication. doi: 10.1093/ntr/ntt109

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. Advance online publication. doi: 10.1037/a0034035

Wang, S, Cui, H., & Li, R. (2013). Empirical likelihood inference for semiparametric estimation equations. Science China: Mathematics. 56, 1247 - 1262.

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

Yancy, W. S., Coffman, CJ., Geiselman, P. J., Kolotkin, R. L., Almirall D., Oddone, E. Z.,.., Voils, C.I. (2013). Considering patient diet preference to optimize weight loss: design considerations of a randomized trial investigating the impact of choice. Contemporary Clinical Trials, 35(1), 106-116. PMC Journal- In Process

Yao, W., & Li, R. (2013). New local estimation procedure for nonparametric regression function of longitudinal data. Journal of Royal Statistical Society, Series B. 75, 123-138. PMCID: PMC3607647

Zhao, Z., Zhang, Y., & Li, R. (2014). Non-parametric estimation under strong dependence. Journal of Time Series Analysis, 35, 4-15. Advance online publication. doi: 10.1111/jtsa.12044

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

Zhu, L, Li, R., & Cui, H. (2013). Robust estimation for partially linear models with large dimensional covariates. Science China: Mathematics, 56. 2069-2088.

Buu, A., Li, R., Tan, X., & 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. PMCID: PMC3505239

Chen, Z., Li, R., & Wu, Y. (2012). Weighted quantile regression for AR model with infinite variance errors. Journal of Nonparametric Statistics, 24, 715 - 731. PMCID: PMC3595619

Das, K., Li, R., Huang, Z., Gai, J., & Wu, R. (2012). A Bayesian framework for functional mapping through joint modeling of longitudinal and time-to-event data. International Journal of Plant Genomics2012. doi:10.1155/2012/680634. PMCID: PMC3364578

Fan, Y., & Li, R. (2012). Variable selection in linear mixed effects models. Annals of Statistics, 40, 2043 - 2068. PMC Journal- In Process

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

Shiyko, M. P., Lanza, S. T., Tan, X., Li, R., & Shiffman, S. (2012). Using the time-varying effect 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. PMCID:PMC3171604

Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2012). A time-varying effect model for intensive longitudinal data. Psychological Methods, 17(1), 61-77. PMCID: PMC3288551

Wang, L., Wu, Y., & Li, R. (2012). Quantile regression for analyzing heterogeneity in ultra-high dimension. Journal of the American Statistical Association, 107, 214-222. PMCID:PMC3471246

Wang, Y., Huang, C., Fang, Y., Yang, Q., & Li, R. (2012). Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing. Journal of the Royal Statistical Society, Series C, 61, 1-24. PMCID:PMC3348702

Yao, W., Lindsay, B. G., & Li, R. (2012). Local modal regression. Journal of Nonparametric Statistics, 24, 647-663. PMCID: PMC3462466

Zhu, H., Li, R., & Kong, L. (2012). Multivariate varying coefficient model for functional responses. Annals of Statistics, 40, 2634 - 2666. PMCID: PMC3464104

Zhu, L., Huang, M., & Li, R. (2012). Semiparametric quantile regression with high dimensional covariates. Statistica Sinica. 22, 1379-1401. PMCID: PMC3109949

Buu, A., Johnson, N. J., Li, R., & 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. PMCID: PMC3133860

Fu, G., Wang, Z., Li, J., Das, K., Li, R., & Wu, L. (2011). A dynamic model for functional mapping of biological rhythms. Journal of Biological Dynamics, 5, 84-101. PMCID: PMC3027063

Kai, B., Li, R., & Zou, H. (2011). New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models. Annals of Statistics, 39, 305-332. PMCID: PMC3109949

Kim, K., Senturk, D., & Li, R. (2011). Recent history functional linear models for sparse longitudinal data. Journal of Statistical Planning and Inference 141(4), 1554-1566PMCID:PMC3117473

Li, J., Das, K., Fu, G., Li, R., & Wu, R. (2011). The Bayesian LASSO for genome-wide association studies. Bioinformatics 27, 516-523. PMCID: PMC3105480

Tan, X., Dierker, L., Rose, J., Li, R. & The Tobacco Etiology Research Network (TERN). (2011). How spacing of data collection may impact estimates of substance use trajectories. Substance Use and Misuse, 46(6), 758-768. PMCID: PMC3107528

Wang, Y., Xu, M., Wang, Z., Tao, M., Zhu, J., Li, R., Wang, L., Berceli, S. A., & Wu, R. (2011). How to cluster gene expression dynamics in response to environmental signals. Briefings in Bioinformatics, 13, 162-174. PMCID: PMC3294239

Zhu, H., Kong, L., Li, R., Styner, M., Gerig, G., Lin, W., & Gilmore, J.H. (2011). FADTTS: Functional analysis of diffusion tensor tract statistics. Neuroimage 56(3), 1412-1425. PMCID:PMC3085665

Zhu, L., Li, L., Li, R., & Zhu, L.-X. (2011). Model-free feature screening for ultrahigh dimensional data. Journal of the American Statistical Association, 106, 1464-1475. PMCID: PMC3384506

 

Presentations

Li, R. (2013, October). Feature selection for varying coefficient models with ultrahigh dimensional covariates. University of Chicago; Florida State University; University of Pennsylvania.

Li, R. (2013, July). Calibrating nonconvex penalized regression in ultrahigh dimension. The Second Taihu International Statistics Forum, Suzhou, P.R. China.

Li, R. (2013, June). Nonparametric mixture regression. The 7th International Workshop on Frontier of Statistics, Beijing, P.R. China.

Li, R. (2013, June). New statistical procedures for analyzing big data. Invited talk at The Spring Research Conference, University of California at Los Angeles, Los Angeles, CA.

Li, R. (2013, May). Feature selection for varying coefficient models with ultrahigh dimensional covariates. Keynote address at the Workshop on Biostatistics and Bioinformatics. Georgia State University, Atlanta, GA..

Li, R. (2013, May). Feature selection for varying coefficient models with ultrahigh dimensional covariates. Presented at University of California at Riverside, Riverside, CA.

Li, R. (2013, May). Feature selection for varying coefficient models with ultrahigh dimensional covariates. University of California at Riverside.

Li, R. (2013, May). Feature selection for varying coefficient models with ultrahigh dimensional covariates. Sole keynote speaker, Workshop on Biostatistics and Bioinformatics. Georgia State University.

Li, R. (2013, April). Feature screening for ultrahigh dimension data. Presented at the Department of Biostatistics, Yale University, New Haven, CT; also presented at the Department of Statistics, Columbia University, New York, NY.

Li, R. (2013, March). Time-varying coefficient models for longitudinal mixed responses. ENAR2013, Orlando, FL.

Li, R. (2013, February). Feature screening for ultrahigh dimension data. Department of Biostatistics, Harvard University.

Li, R. (2012, December). Feature screening for ultrahigh dimension data. Department of Information and Operations Management, University of Southern California.

Li, R. (2012, December). Penalized quantile regression for in ultra-high dimensional data. University of Illinois at Chicago.

Li, R. (2012, November). Penalized quantile regression for in ultra-high dimensional data. University of Maryland at College Park.

Li, R. (2012, October). Calibrating nonconvex penalized regression in ultrahigh dimension. Presented at the 3rd Princeton Statistics Day, Princeton University.

Li, R. (2012, September). Feature screening via distance correlation learning. Presented at the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta GA.

Li, R. (2012, September). Feature screening via distance correlation learning. Presented at the Department of Statistics, North Carolina State University, Raleigh, NC.

Li, R. (2012, July). Co-chair of scientific program committee for the second IMS Asia Pacific Rim meeting, Tokyo, Japan.

Li, R. (2012, July). New estimation procedure for nonparametric regression function of longitudinal data. Presented at the 2012 International Forum on Modern Statistics and Econometrics. Xiamen University, P.R. China.

Li, R. (2012, July). Nonparametric mixture of regression models. Presented at the Beijing Normal University .

Li, R. (2012, July). Variable selection for linear mixed effect models. Presented at the 2nd Institute of Mathematical Statistics Asia Pacific Rim Meeting, Japan; Beijing Normal University; Shanghai University of Finance and Economics.

Li, R. (2012, July). Variable selection in linear models via regularization: Methods, algorithms and recent developments. Six-hour lecture presented at the Xiamen University, China.

Trail, J. B., Collins, L. M., Rivera, D. E., Li, R., & Piper, M. E. (2012, July). A dynamical systems approach for adaptive intervention development. Presented at the Institute of Mathematical Statistics – Asia Pacific Rim Meeting, Tsukuba, Japan.

Li, R. (2012, June). New local estimation procedure for nonparametric regression function of longitudinal data. Presented at the Institute of Applied Mathematics, Chinese Academy of Sciences.

Li, R. (2012, June). Nonparametric mixture of regression models. Presented at the Institute of Applied Mathematics, Chinese Academy of Sciences.

Li, R. (2012, June). Organize an invited talk session on Recent Advances on variable selection and regularization methods. Presented at the First Conference of the International Society for Nonparametric Statistics, Chalkidiki, Greece.

Li, R. (2012, June). Varying-coefficient models for data with auto-correlated error process. Presented at the Institute of Applied Mathematics, Chinese Academy of Sciences.

Selya, A. S., Dierker, L. C., Rose, J. S., Hedeker, D., Tan, X., Li, R., & Mermelstein, R. J. (2012, June). Time-varying effects of nicotine dependence on adolescent smoking. Poster presented at the 20th Annual Meeting of the Society for Prevention Research, Washington, DC.

Shiyko, M. S., Shiffman, S., & Li. R. (2012, June). The time-varying lagged effects of momentary negative affect on smoking urges:  Application of the time-varying effect model (TVEM). In Vasilenko, S. A. (Chair), Uncovering the dynamics of smoking cessation processes: New approaches to analysis of ecological momentary assessment data. Presented at the 20th Annual Meeting of the Society for Prevention Research, Washington, DC.

Trail, J. B., Collins, L. M., Rivera, D. E., Li, R., & Piper, M. E. (2012, June). A dynamical systems approach for adaptive intervention development. In Collins, L. M. (Chair), Building Optimized Prevention Interventions. Special Interest Group. Presented at the 20th Annual Meeting of the Society for Prevention Research, Washington, DC.

Vasilenko, S. A., Liu, X. Lanza, S. T., Piper, M. & Li, R. (2012, June). Smoking cessation fatigue and in relapsers and successful quitters using a time-varying effects model. In Vasilenko, S. A. (Chair), Uncovering the dynamics of smoking cessation processes: New approaches to analysis of ecological momentary assessment data. Presented at the 20th Annual Meeting of the Society for Prevention Research, Washington, DC.

Dziak, J. J., Li, R., Tan, X., Lanza, S. T., & Shiffman, S. (2012, March). Nonlinear latent class growth modeling of affect during smoking cessation. Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Kurum, E. Li, R., & Shiffman, S. (2012, March). Joint modeling of longitudinal binary and continuous responses. Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Li, R. (2012, March). New statistical models and techniques for analyzing ecological momentary assessment (EMA) data. Presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Liu, X., Li, R., Lanza, S. T., Piper, M. E., & Vasilenko, S. A. (2012, March). Using TVEM to examine cessation fatigue in smoking cessation study—Differences between placebo and treatment groups. Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Selya, A., Dierker, L. C., Rose, J. S., Tan, X., Li, R., & Mermelstein, R. J. (2012, March). Time-Varying effects of nicotine dependence, tobacco exposure, and parental smoking on the regularity of adolescent smoking. Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Shiyko, M. S., Li. R., & Shiffman, S. (2012, March). The time-varying lagged effects of momentary negative affect on smoking urges:  Application of the time-varying effect model (TVEM). Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Trail, J. B., Collins, L. M., Rivera, D. E., Li, R., & Piper, M. E. (2012, March). A dynamical systems analysis of smoking cessation data. Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Vasilenko, S. A., Lanza, S. T., Liu, X., Piper, M. E., Yang, J., & Li, R. (2012, March). Time-Varying predictors of lapse after smoking cessation attempt. Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Yang, J., Tan, X., Li, R., & Lanza, S. T. (2012, March). Generalized time-varying effects models for intensive longitudinal data. Poster presented at The Annual Meeting of the Society for Research on Nicotine and Tobacco, Houston, TX.

Li, R. (2011, November). Feature screening via distance correlation learning. Presented at the Department of Operation Research and Financial Engineering, Princeton University.

Li, R. (2011, October). High and ultrahigh dimensional data analysis. Lecture presented at the Institute of applied mathematics, Chinese Academy of Sciences, jointly with Capital Normal University and Beijing Normal University.

 Li, R. (2011, October). Variable selection and regularization methods. Lecture presented at the Capital Normal University jointly with the Beijing Normal University and the Institute of Applied Mathematics, Chinese Academy of Sciences.

Li, R. (2011, August). Sparse quantile regression Approach for analyzing heterogeneity in ultrahigh dimension. Paper presented at the Joint Statistical Meeting, Miami, FL.

Li, R. (2011, July). Sparse quantile regression approach for analyzing heterogeneity in ultrahigh dimension. Paper presented at the First Wu Xi International Statistics Forum, Wuxi, P.R., China.

Dziak, J. J., Huang, L., Lanza, S. T., Li, R., Collins, L. M., & Xu, S. (2011, June). Software advances from the Methodology Center at Penn State. Technology demonstration presented at the Society for Prevention Research Annual Meetings, Washington, DC.

Dziak, J. J., Coffman, D. L., Lanza, S. T., & Li, R. (2011, June). Sensitivity and specificity of information criteria for model selection in prevention and psychology datasets. Poster prsented at the Society for Prevention Research Annual Meetings, Washington, DC.

Shiyko, M., Lanza, S. T., Tan, X., Shiffman, S., & Li, R. (2011, June).  Between-group differences in temporal dynamics of negative affect, self-confidence, and smoking urges in short-term successful quitters and relapsers: Applications of the model with varying effects (MOVE). In M. Shiyko (Chair), Applications of novel methods for analysis of intensive longitudinal data in studies on drug use. Symposium presented at the Society for Prevention Research Annual Meetings, Washington, DC.

Shiyko, M., Li, R., Lin, J., & Ostroff, J. (2011, June). Joint modeling of longitudinal trajectories and time-to-event analysis in the presence of drop-out. Poster presented at the Society for Prevention Research Annual Meetings, Washington, DC.

Tan, X., Shiyko, M., Li, R., Li, Y., & Dierker, L. (2011, June). Model with varying effects (MOVE) for describing time-varying relationship in covariates in intensive longitudinal data studies: Learning to ask new research questions. Poster presented at the Society for Prevention Research Annual Meetings, Washington, DC.

 

Software and Documentation

TVEM SAS Macro Suite (Version 2.0.0) [Software]. (2012). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu

Yang, J., Tan, X., Li, R., & Wagner, A. (2012). TVEM (time-varying effect model) SAS macro suite users' guide (Version 2.0.0). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu

Li, R., & Tan, X. (2011). TVEM (Time‐Varying effect model) SAS macro users' guide (Version 1.2.0). University Park: The Methodology Center, Penn State. Retrieved from http://methodology.psu.edu

TVEM (Time‐Varying Effect Model) SAS macro (Version 1.2.0) [Software]. (2011). University Park: The Methodology Center, Penn State. Retrieved from

http://methodology.psu.edu

Dziak, J. J., Lemmon, D. R., Li, R., & Huang, L. (2010, May). PROC SCADGLIM User's Guide Version 1.1 beta. University Park: The Methodology Center, Penn State. Available at http://methodology.psu.edu.

Dziak, J. J., Lemmon, D. R., Li, R., & Huang, L. (2010, May). PROC SCADLS User's Guide Version 1.1. beta. University Park: The Methodology Center, Penn State. Available at http://methodology.psu.edu.

Li, R. (2010, January). SAS Macros for Estimation Functional Hierarchical Linear Models (FHLM) Using Local Linear Regression Estimation Procedure: %FHLMLLR Version 1.1. [Software]. University Park: The Methodology Center, Penn State. Available at http://methodology.psu.edu.

Li, R. & Tan, X. (2010, January). %FHLMLLR User's Guide Version 1.1. University Park: The Methodology Center, Penn State. Available at http://methodology.psu.edu.

Li, R. & Tan, X. (2010, January). MOVEPSPline and MOVEBSpline User’s Guide Version 1.1. University Park: The Methodology Center, Penn State. Available at http://methodology.psu.edu.

Tan, X. & Li, R. (2010, January). SAS Macro for Estimation of Model with Varying Effect (MOVE): %MOVEBSpline Version 1.1. [Software]. University Park: The Methodology Center, Penn State. Available at http://methodology.psu.edu.

Tan, X. & Li, R. (2010, January). SAS Macro for Estimation of Model with Varying Effect (MOVE): %MOVEPSpline Version 1.1. [Software]. University Park: The Methodology Center, Penn State. Available at http://methodology.psu.edu.

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