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Runze Li, Eberly Family Chair Professor |
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Fang, K.-T., Li, R. and Sudjianto, A. (2006). Design and Modeling for Computer Experiments. Chapman and Hall/CRC. Boca Raton, FL.
Fan, J. Li, R., Zhang, C.-H. and Zou, H. (2020). Statistical Foundations of Data Science. Chapman and Hall/CRC. Boca Raton, FL.
He, Z., Sun, Y., Liu, J. and Li, R. (2024). TransFusion: Covariate-shift robust transfer learning for high-dimensional regression. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024, Valencia, Spain. PMLR: Volume 238. [pdf]
Guo, X., Li, R., Zhang, Z. and Zou, C. (2024). Model-free statistical inference on highdimensional data. Journal of American Statistical Association. In press. [Link] .
Wang, J., Cai, X., Niu, X. and Li, R. (2024). Variable selection for high-dimensional nodal attributes in social networks with degree heterogeneity. Journal of American Statistical Association. In press. [Link] .
Wen, J., Yang, S., Wang, D., Jiang., Y. and Li, R. (2024). Feature-splitting algorithms for ultrahigh dimensional quantile regression. Journal of Econometrics. In press. [Link] .
Zhou, Y., Xu, K., Zhu, L. and Li, R. (2024). Rank-based indices for testing independence between two high-dimensional vectors. Annals of Statistics, 52, 184-206. [Link] .
Liu, W., Yu, X., Zhong, W. and Li, R. (2024). Projection test for mean vector in high dimensions. Journal of American Statistical Association, 119, 744 - 756. [Link] .
Chen, Y., Wang, Y., Fang, E.X., Wang, Z. and Li, R. (2024). Nearly dimension-independent sparse linear bandit over small action spaces via best subset selection. Journal of American Statistical Association, 119, 246-258. [Link] .
Tong, Z, Cai, Z., Yang, S. and Li, R. (2023). Model-free conditional feature screening with FDR control. Journal of American Statistical Association, 118, 2575-2587. [Link] .
Yu, X., Li, D., Xue, L. and Li, R. (2023). Power-enhanced simultaneous test of high-dimensional mean vectors and covariance matrices with application to gene-set testing. Journal of American Statistical Association, 118, 2548-2561. [Link] .
Li, R., Xu, K., Zhou, Y. and Zhu, L. (2023). Test the effects of high-dimensional covariates via aggregating cumulative covariances. Journal of American Statistical Association, 118, 2548-2561. [Link] .
Sheng, B., Li, C., Bao, L. and Li, R. (2023). Probabilistic HIV recency classification - a logistic regression without labelled individual level training data. Annals of Applied Statistics, 17, 108-129. [Link] .
Guo, X, Li, R, Liu, J. and Zeng, M. (2023). Statistical Inference for Linear Mediation Models with High-dimensional Mediators and Application to Studying Stock Reaction to COVID-19 Pandemic. Journal of Econometrics, 235, 166-179. [Link] .
Guo, X., Ren, H., Zou, C. and Li, R. (2022). Threshold selection for feature screening via error rate control. Journal of American Statistical Association, 117, 1110-1121. [Link] .
Li, C. and Li, R. (2022). Linear hypothesis testing in linear models with high dimensional responses. Journal of American Statistical Association, 117, 1738-1750. [Link] .
Zou, T, Lan, W, Li, R. and Tsai, C.-L. (2022). Inference on Covariance-Mean Regression. Journal of Econometrics, 230, 318-338. [Link] .
Nandy, D., Chiaromonte, F. and Li, R. (2022). Covariate information number for feature screening in ultrahigh-dimensional supervised problems. Journal of American Statistical Association, 117, 1516-1529. [Link] .
Guo, X., Li, R., Liu, J. and Zeng, M. (2022). High-dimensional mediation analysis for selecting DNA methylation Loci mediating childhood trauma and cortisol stress reactivity. Journal of American Statistical Association, 117. 1110-1121. [Link] .
Ren, H., Zou, C., Chen, N. and Li, R. (2022). Large-scale datastreams surveillance via pattern-oriented-sampling. Journal of American Statistical Association, 117, 794 - 808. [Link] .
Liu, W. Ke, Y., Liu, J. and Li, R. (2022). Model-free feature screening and FDR control with knockoff features. Journal of American Statistical Association, 117, 428 - 443. [Link] .
Liu, W., Yu, X. and Li, R. (2022). Multiple-splitting project test for high dimensional mean vectors. Journal of Machine Learning and Research 23(71), 1-27. [Link] .
Cai, Z., Li, R. and Zhang, Y. (2022). A distribution free conditional independence test with applications to causal discovery. Journal of Machine Learning and Research, 23(85), 1-41. [Link] .
Huang, Y., Li, C., Li, R. and Yang, S. (2022). An overview of tests on high-dimensional means. Journal of Multivariate Analysis, 188, 104813. [Link] .
Li, Z., Wang, Q. and Li, R. (2021). Central limit theorem for linear spectral statistics of large dimensional Kendall's rank correlation matrices and its applications. Annals of Statistics, 49, 1569 - 1593. [Link] .
Shi, C., Song, R., Lu, W. and Li, R. (2021). Statistical inference for high-dimensional models via recursive online-score estimation. Journal of American Statistical Association, 116. 1307 - 1318. [Link] .
Xiao, D., Ke, Y. and Li, R. (2021). Homogeneity structure learning in large-scale panel data with heavy-tailed errors. Journal of Machine Learning Research, 22(13):1-42. [Link] .
Huang, D., Zhu, X., Li, R. and Wang, H. (2021). Feature screening for network data. Statistica Sinica, 31, 1239 - 1259. [Link] .
Wang, J., Cai, X. and Li, R. (2021). Variable selection for partially linear models via Bayesian subset modeling with diffusing prior. Journal of Multivariate Analysis, 183, 104733. [Link] .
Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020). A new tuning-free approach to high-dimensional regression (with discussions). Journal of American Statistical Association, 115, 1700 - 1729. [pdf] [Supplement] [Comment1] [Comment2] [Comment3] [Rejoinder]
Fang, X. E., Ning, Y. and Li, R. (2020). Test of significance for high-dimensional longitudinal data. Annals of Statistics, 48, 2622 - 2645. [pdf] [Supplement]
Zhou, T., Zhu, L., Xu, C. and Li, R. (2020). Model-free forward regression via cumulative divergence. Journal of American Statistical Association, 115. 1393 - 1405. [pdf] [Supplement]
Zou, C., Wang, G. and Li, R. (2020). Consistent selection of the number of change-points via sample-splitting. Annals of Statistics, 48, 413 -439. [pdf] [Supplement]
Cui, X., Li, R., Yang, G. and Zhou, W. (2020). Empirical likelihood test for large dimensional mean vector. Biometrika, 107, 591 - 607. [pdf] [Supplement]
Wang, L., Chen, Z., Wang, C. D. and Li, R. (2020). Ultrahigh dimensional precision matrix estimation via refitted cross validation. Journal of Econometrics, 215, 118 - 130. [pdf]
Li, X., Li, R., Xia, Z. and Xu, C. (2020). Distributed feature screening via componentwise debiasing. Journal of Machine Learning and Research, 21(24). 1 - 32. [pdf]
Chu, W., Li, R., Liu, J. and Reimherr, M. (2020). Feature screening for generalized varying coefficient mixed effect models with application to obesity GWAS. Annals of Applied Statistics, 14, 276 - 298. [pdf] [Supplement]
Cai, Z, Li, R. and Zhu, L. (2020). Online Sufficient Dimension Reduction Through Sliced Inverse Regression. Journal of Machine Learning and Research, 21(10). 1 - 25. [pdf]
Yang, G., Yang, S. and Li, R. (2020). Feature screening in ultrahigh dimensional generalized varying-coefficient models. Statistica Sinica, 30, 1049 - 1067. [pdf] [Supplement]
Zheng, S., Chen, Z., Cui, H. and Li, R. (2019). Hypothesis testing on linear structures of high dimensional covariance matrix. Annals of Statistics, 47, 3300 - 3334. [pdf] [Supplement]
Shi, C., Song, R., Chen, Z. and Li, R. (2019). Linear hypothesis testing for high dimensional generalized linear models. Annals of Statistics, 47, 2671 - 2703. [pdf] [Supplement]
Zhong, P.-S., Li, R. and Santo, S. (2019). Homogeneity test of covariance matrices and change-points identification with high-Dimensional longitudinal data. Biometrika, 106, 619 - 634. [pdf] [Supplement
Zhu, X., Chang, X., Wang, H. and Li, R. (2019). Portal nodes screening for large scale social networks. Journal of Econometrics, 209, 145- 157. [pdf] [Supplement]
Liu, H., Wang, X., Yao, T., Li, R. and Ye, Y. (2019). Sample average approximation with sparsity-inducing penalty for high-dimensional stochastic programming. Mathematical Programming, 78, 69-108.
Chen, Z., Fan, J. and Li, R. (2018). Error variance estimation in ultrahigh dimensional additive models. Journal of American Statistical Association, 113, 315 - 327. [pdf]
Li, R., Ren, J.J., Yang, G. and Ye, Y. (2018). Asymptotic behavior of Cox's partial likelihood and its application to variable selection. Statistica Sinica, 28, 2713 - 2731. [pdf]
Liu, J., Lou, L. and Li, R. (2018). Variable Selection for Partially Linear Models via Partial Correlation. Journal of Multivariate Analysis, 67, 418 - 434.
Ma, S., Li, R. and Tsai, C.-L. (2017). Variable screening via partial quantile correlation. Journal of American Statistical Association, 112, 650 - 663. [pdf] [Supplement]
Zhu, L., Xu, K., Li, R. and Zhong, W. (2017). Project correlation between two random vectors. Biometrika, 104, 829 - 843. [pdf]
Liu, H., Yao, T, Li, R. and Ye, Y. (2017). Folded concave penalized sparse linear regression: complexity, sparsity, statistical performance, and algorithm theory for local solutions. Mathematical Programming SERIES A, 166, 207 - 240. [pdf]
Li, R., Liu, J. and Lou, L. (2017). Variable selection via partial correlation. Statistica Sinica, 27, 983 -996. [pdf] [Supplement]
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). Ultrahigh dimensional multiclass Linear discriminant analysis by pairwise sure independence screening. 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]
Nam, J. K., Piper, M. E., Tong, Z., Li, R., Yang, J. J., Jorenby, D.E., and Buu, A. (2023) Dependence motives and use contexts that predicted smoking cessation and vaping cessation: a two-year longitudinal study with 13 waves, Drug and Alcohol Dependence, 250, 110871. doi.org/10.1016/j.drugalcdep.2023.110871.
Buu, A., Tong, Z., Cai, Z., Li, R., Yang, J.J., Jorenby, D.E., and Piper, M.E. (2023). Subtypes of dual users of combustible and electronic cigarettes: longitudinal changes in product use and dependence symptomatology. Nicotine & Tobacco Research, 25, 438-443. https://doi.org/10.1093/ntr/ntac151.
Coffman, D.L., Dziak, J.J., Litson, K., Chakraborti, Y., Piper, M.E., and Li, R. (2022). A causal approach to functional mediation analysis with application to a smoking cessation intervention. Multivariate Behaviorial Research, 58, 859-876. https://doi.org/10.1080/00273171.2022.2149449
Rincon, S. J., Dou, N., Murray-Kolb, L. E., Hudy, K. A., Mitchell, D. C., Li, R. and Na, M. (2022). Daily food insecurity is associated with diet quality, but not energy intake, in winter and during COVID-19, among low-income adults. Nutrition Journal, 21:19 https://doi.org/10.1186/s12937-022-00768-y
Na, M., Dou, N., Liao, Y, Rincon, S. J., Francis, L. A., Graham-Engeland, J. E., Murray-Kolb, L. E. and Li, R (2022). Daily food insecurity predicts lower positive and higher negative affect: An ecological momentary assessment study. Frontiers in Nutrition. https://doi.org/10.3389/fnut.2022.790519
Parikh, R. Liu, M., Li, E., Li, R. and Chen, J. (2021). Trajectories of mortality risk among patients with cancer and associated end-of-life utilization. Nature Partner Journals (npj) Digital Medicine. 4 (104). https://doi.org/10.1038/s41746-021-00477-6.
Buu, A., Cai, Z., Li, R., Wong, S.W., Lin, H.C., Su, W.C., Jorenby, D.E., and Piper, M.E. (2021). Validating e-cigarette dependence scales based on dynamic patterns of vaping behaviors. Nicotine & Tobacco Research, 23, 1484 - 1489. https://doi.org/10.1093/ntr/ntab050
Buu, A., Cai, Z., Li, R., Wong, S., Lin, H., Su, W., Jorenby, D.E., and Piper, M.E. (2021). The association between short-term emotion dynamics and cigarette dependence: a comprehensive examination of dynamic measures. Drug and Alcohol Dependence, 218, 108341. https://doi.org/10.1016/j.drugalcdep.2020.108341
Coffman, D., Cai, X., and Li, R. (2020). Challenges and opportunities in collecting and modeling ambulatory electrodermal activity data. JMIR Biomedical Engineering, , e17106. https://doi.org/10.2196/17106
Buu, A., Yang, S., Li, R., Zimmerman, M.A., Cunningham, R.M., and Walton, M.A. (2020). Examining measurement reactivity in daily diary data on substance use: results from a randomized experiment. Addictive Behaviors, 102, 106198. https://doi.org/10.1016/j.addbeh.2019.106198.
Trucco, E. M., Yang, S., Yang, J. J., Zucker, R. A., Li, R. and Buu, A. (2020). Time-varying Effects of GABRG1 and Maladaptive Peer Behavior on Externalizing Behavior from Childhood to Adulthood: Testing Gene x Environment x Development Effects. Journal of Youth and Adolescence, 49, 1351 - 1364.
Liu, W., Li, R., Zimmerman, M.A., Walton, M.A., Cunningham, R.M., and Buu, A. (2019). Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use. Addictive Behaviors: Special Issue on Improving the Implementation of Quantitative methods in Addiction Research, 94, 147 - 155
Dziak, J.J., Coffman, D. L., Reimherr, M., Petrovich, J., Li, R. and Shiffman, S. (2019). Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists. Statistical Survey, 13, 150 -180.
Dziak, J., Coffman, D. L., Lanza, S. T., Li, R. and Jermiin, L. S. (2019). Sensitivity and specificity of information criteria. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbz016
Wang, L., Ma, J., Dholakia, R., Howells, C., Lu, Y., Chen, C., Li, R., Murray, M. and Leslie, D. (2019). Changes in healthcare expenditures after the autism insurance mandate. Research in Autism Spectrum Disorders, 57, 97 -104.
Dierker, L., Selya, A., Lanza, S., Li, R. and Rose, J. (2018). Depression and marijuana use disorder symptoms among current marijuana users. Addictive Bahaviors, 76, 161 - 168.
Yang, S., Cranford, J. A., Li, R., Zucker, R. A. and Buu, A. (2017). Time-varying effect model for studying gender differences in health behavior. Statistical Methods in Medical Research. 26, 2812 - 2820
Yang, S., Cranford, J. A., Jester, J. M., Li, R., Zucker, R. A. and Buu, A. (2017). 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, 36, 827 - 837.
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.
Yang, S., Wen, J., Eckert, S. T., Wang, Y., Liu, D., Wu, R., Li, R. and Zhan, X. (2020). Prioritizing genetic variants in GWAS using permutation-assisted lasso tuning. Bioinformatics, 36, 3811- 3817.
Dziak, J., Coffman, D. L., Lanza, S. T., Li, R. and Jermiin, L. S. (2020). Sensitivity and specificity of information criteria. Briefings in Bioinformatics, 21, 553-565.
Miao, J. Chen, Z. Sebastian, A. Wang, Z., Shrestha, S., Li, X., Praul, C., Albert, I., Li, R. and Cui, L. (2017). Sex-specific biology of the human malaria parasite revealed from transcriptomes and proteomes of male and female gametocytes. Molecular and Cellular Proteomics, 16, 537 - 551.
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
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
Brown, G., Du, G., Farace, E., Lewis, M. M., Eslinger, P. J., McInerney, J., Kong, L., Li, R., Huang, X., and De Jesus, S., (2022). Subcortical iron accumulation pattern may predict neuropsychological outcomes after STN 3 DBS: a pilot study. Journal of Parkinson's Disease. https://doi.org/10.3233/JPD-212833
Li, C., Wang, X., Du, G, Chen, H, Brown, G., Lewis, M.M., Yao, T., Li, R., Huang, X. (2021). Folded concave penalized learning in identifying high-dimensional MRI markers for Parkinson's disease: a benchmark of whole brain MRI markers. Journal of Neuroscience Methods, 357, 109157. https://doi.org/10.1016/j.jneumeth.2021.109157
Du, G., Lewis, M. M., Kanekar, S., Sterling, N. W., He, L., Kong, L, Li, R. and Huang, X. (2017). Combined diffusion tensor imaging and R2* differentiate Parkinson's disease and atypical Parkinsonism. American Journal of Neuroradiology, 38, 966-972.
Zhang, L., Wang, X., Wang, M., Sterling, N. W., Du, G. Lewis, M. M., Yao, T., Mailman, R. B., Li, R. Huang, X. (2017). Circulating cholesterol levels may link to the factors influencing Parkinson's risk. Frontiers in Neurology, 8, 501.
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.
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