Publication

2021–present

• Shi, C., Zhou, Y., and Li, L. (2024+). Testing directed acyclic graph via structural, supervised and generative adversarial learning. Journal of the American Statistical Association, accepted.

• Lee, K.Y., Li, L., and Li, B. (2024+). Functional directed acyclic graphs. Journal of Machine Learning Research, accepted.

• Lyu, X., Kang, J., and Li, L. (2024+). High-dimensional multi-subject time series transition matrix inference with application to brain connectivity analysis. Biometrics, accepted.

• Jiang, F., Tian, L., Kang, J., and Li, L. (2024+). High-dimensional subgroup regression analysis. Statistica Sinica, accepted.

• Lee, C.E, Zhang, X., and Li, L. (2024+). Mean dimension reduction and testing for nonparametric tensor response regression. Statistica Sinica, accepted.

• Dai, X. and Li, L. (2024). Post-regularization confidence bands for ordinary differential equations. Journal of Machine Learning Research, 25, 1-51.

• Zhang, D., Li, L., Sripada, C., and Kang, J. (2023). Image response regression via deep neural networks. Journal of the Royal Statistical Society, Series B., 85, 1589-1614.

• Zhou, Y., Shi, C., Li, L., and Yao, Q. (2023). Testing for the Markov property in time series via deep conditional generative learning. Journal of the Royal Statistical Society, Series B., 85, 1204-1222.

• Dai, X., Lyu, X., and Li, L. (2023). Kernel knockoffs selection for nonparametric additive models. Journal of the American Statistical Association, 118, 2158-2170.

• Li, L., Zeng, J., and Zhang, X. (2023). Generalized liquid association analysis for multimodal neuroimaging. Journal of the American Statistical Association, 118, 1984-1996.

• Dai, X. and Li, L. (2023). Orthogonalized kernel debiased machine learning for multimodal data analysis. Journal of the American Statistical Association, 118, 1796-1810.

• Lee, K.Y., Li, L., Li, B., and Zhao, H. (2023). Nonparametric functional graphical modeling through functional additive regression operator. Journal of the American Statistical Association, 118, 1718-1732.

• Tang, X. and Li, L. (2023). Multivariate temporal point process regression. Journal of the American Statistical Association, 118, 830-845.

• Zhou, J., Sun, W.W., Zhang, J., and Li, L. (2023). Partially observed dynamic tensor response regression. Journal of the American Statistical Association, 118, 424-439.

• Lee, K.Y., Ji, D., Li, L., Constable, T., and Zhao, H. (2023). Conditional functional graphical models. Journal of the American Statistical Association, 118, 257-271.

• Zhang, J., Sun, W.W., and Li, L. (2023). Generalized connectivity matrix response regression with applications in brain connectivity studies. Journal of Computational and Graphical Statistics, 32, 252-262.

• Lyu, X., Kang, J., and Li, L. (2023). Statistical inference for high-dimensional vector autoregression with measurement error. Statistica Sinica, 33, 1435-1459.

• Zhou, Y., Shi, C., Qi, Z., and Li, L. (2023). Optimizing pessimism in dynamic treatment regimes: a Bayesian learning approach. Proceedings of Machine Learning Research, 206, 1-18.

• Li, Q., and Li, L. (2022). Integrative factor regression and its inference for multimodal data analysis. Journal of the American Statistical Association, 117, 2207-2221.

• Shi, C., and Li, L. (2022). Testing mediation effects using logic of Boolean matrices. Journal of the American Statistical Association, 117, 2014-2027.

• Dai, X., and Li, L. (2022). Kernel ordinary differential equations. Journal of the American Statistical Association, 117, 1711-1725.

• Lee, K.Y., and Li, L. (2022). Functional structural equation model. Journal of the Royal Statistical Society, Series B., 84, 600-629.

• Lee, K.Y. and Li, L. (2022). Functional sufficient dimension reduction through average Frechet derivatives. The Annals of Statistics, 50, 904–929.

• Li, L., Shi, C., Guo, T., and Jagust, W.J. (2022). Sequential pathway inference for multimodal neuroimaging analysis. Stat, 11:e433.

• Liu, Y., Li, L., and Wang, X. (2022). A nonlinear sparse neural ordinary differential equation model for multiple functional processes. The Canadian Journal of Statistics, 50, 59-85.

• Luo, L. and Li, L. (2022). Online two-way estimation and inference via linear mixed-effects models. Statistics in Medicine, 41, 5113–5133.

• Xia, Y., and Li, L. (2022). Hypothesis testing for network data with power enhancement. Statistica Sinica, 32, 293-321.

• Virta, J., Lee, K.Y., and Li, L. (2022). Sliced inverse regression in metric spaces. Statistica Sinica, 32, 2315-2337.

• Zhao, Y., and Li, L. (2022). Multimodal data integration via mediation analysis with high-dimensional exposures and mediators. Human Brain Mapping, 43, 2519–2533.

• Shi, C., Xu, T., Bergsma, W., and Li, L. (2021). Double generative adversarial networks for conditional independence testing. Journal of Machine Learning Research, 22, 1-32.

• Sun, W.W., Hao, B., and Li, L. (2021). Tensor data analysis. Wiley StatsRef: Statistics Reference Online, 1-26.

• Wang, Y.R., Li, L., Li, J.J. and Huang, H. (2021). Network modeling in biology: statistical methods for gene and brain networks. Statistical Science, 36, 89-108.

• Ye, Y., Xia, Y., and Li, L. (2021). Paired test of matrix graphs and brain connectivity analysis. Biostatistics, 22, 402-420.

• Zhao, Y., Li, L., and Caffo, B.S. (2021). Multimodal neuroimaging data integration and pathway analysis. Biometrics, 77, 879-889.

2016–2020

• Xia, Y., Li, L., Lockhart, S.N., and Jagust, W.J. (2020). Simultaneous covariance inference for multimodal integrative analysis. Journal of the American Statistical Association, 115, 1279-1291.

• Zhang, J., Sun, W.W., and Li, L. (2020). Mixed-effect time-varying stochastic blockmodel and application in brain connectivity analysis. Journal of the American Statistical Association, 115, 2022-2036.

• Kim, K., Li, B., Yu, Z., and Li, L. (2020). On post dimension reduction statistical inference. The Annals of Statistics, 48, 1567-1592.

• Wang, M., and Li, L. (2020). Learning from binary multiway data: probabilistic tensor decomposition and its statistical optimality. Journal of Machine Learning Research, 21, 1-38.

• Guo, X., Li, L., and Wu, Q. (2020). Modeling interactive components by coordinate kernel polynomial models. Mathematical Foundations of Computing, 3, 263-277.

• Sun, W.W., and Li, L. (2019). Dynamic tensor clustering. Journal of the American Statistical Association, 114, 1894-1907.

• Wang, W., Zhang, X., and Li, L. (2019). Common reducing subspace model and network alternation analysis. Biometrics, 75, 1109-1120.

• Zhang, X., Li, L., Zhou, H., Zhou, Y., and Shen, D. (2019). Tensor generalized estimating equations for longitudinal imaging analysis. Statistica Sinica, 29, 1977-2005.

• Xia, Y. and Li, L. (2019). Matrix graph hypothesis testing and application in brain connectivity alternation detection. Statistica Sinica, 29, 303-328.

• Li, L., Kang, J., Lockhart, S.N., Adams, J., and Jagust, W. (2019). Spatially adaptive varying correlation analysis for multimodal neuroimaging data. IEEE Transactions on Medical Imaging, 38, 113-123.

• Zhu, Y. and Li, L. (2018). Multiple matrix Gaussian graphs estimation. Journal of the Royal Statistical Society, Series B., 80, 927-950.

• Li, Q., and Li, L. (2018). Integrative linear discriminant analysis with guaranteed error rate improvement. Biometrika, 105, 917-930.

• Li, X., Xu, D., Li, L., and Zhou, H. (2018). Tucker tensor regression and neuroimaging analysis. Statistics in Biosciences, 10, 520-545.

• Adams J.N., Lockhart, S.N., Li, L., and Jagust, W.J. (2018). Relationships between tau and glucose metabolism reflect Alzheimer’s disease pathology in cognitively normal older adults. Cerebral Cortex, 29, 1997-2009.

• Li, L. (2018). Sufficient dimension reduction. Wiley StatsRef: Statistics Reference Online, 1-8.

• Li, L. and Zhang, X. (2017). Parsimonious tensor response regression. Journal of the American Statistical Association, 112, 1131-1146.

• Sun, W.W. and Li, L. (2017). Sparse tensor response regression and neuroimaging analysis. Journal of Machine Learning Research, 18, 4908-4944.

• Zhang, X. and Li, L. (2017). Tensor envelope partial least squares regression. Technometrics, 59, 426-436.

• Xia, Y. and Li, L. (2017). Hypothesis testing of matrix graph model with application to brain connectivity analysis. Biometrics, 73, 780-791.

• Li, Z., Suk, H-I., Shen, D., and Li, L. (2016). Sparse multi-response tensor regression for Alzheimer's disease study with multivariate clinical assessments. IEEE Transactions on Medical Imaging, 35, 1927-1936.

• Kang, J. and Li, L. (2016). Discussion of “Fiber direction estimation, smoothing and tracking in diffusion MRI” by R. Wong, et al. The Annals of Applied Statistics, 10, 1162-1165.

2011–2015

• Guo, Z., Li, L., Lu, W., and Li, B. (2015). Groupwise dimension reduction via envelope method. Journal of the American Statistical Association, 110, 1515-1527.

• Zhou, H., and Li, L. (2014). Regularized matrix regression. Journal of the Royal Statistical Society, Series B., 76, 463-483.

• Ding, X., Li, L., and Zhu, L.X. (2014). Goodness-of-fit testing-based selection for large-p-small-n problems: a two-stage ranking approach. Journal of Statistical Planning and Inference, 145, 148-164.

• Zhao, J., Leng, C., Li, L., and Wang, H. (2013). High dimensional influence measure. The Annals of Statistics, 41, 2639-2667.

• Zhou, H., Li, L., and Zhu, H. (2013). Tensor regression with applications in neuroimaging data analysis. Journal of the American Statistical Association, 108, 540-552.

• Zhu, H., Li, L., and Zhou, H. (2012). Nonlinear dimension reduction with Wright-Fisher kernel for genotype aggregation and association mapping. Bioinformatics, 28, 375-381.

• Sun, W., and Li, L. (2012). Multiple loci mapping via model-free variable selection. Biometrics, 68, 18-22.

• Li, B., Artemiou, A., and Li, L. (2011). Principal support vector machines for linear and nonlinear sufficient dimension reduction. The Annals of Statistics, 39, 3182-3210.

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

• Reich, B.J., Bondell, H.D., and Li, L. (2011). Sufficient dimension reduction via Bayesian mixture modeling. Biometrics, 67, 886-895.

• Lu, W., and Li, L. (2011). Sufficient dimension reduction for censored regressions. Biometrics, 67, 513-523.

• Zhu, H., and Li, L. (2011). Biological pathway selection through nonlinear dimension reduction. Biostatistics, 12, 429-444.

• Wu, Y., and Li, L. (2011). Asymptotic properties of sufficient dimension reduction with a diverging number of predictors. Statistica Sinica, 21, 707-730.

• Li, L., Zhu, L.P., and Zhu, L.X. (2011). Inference on the primary parameter of interest with the aid of dimension reduction estimation. Journal of the Royal Statistical Society, Series B., 73, 59-80.

• Shao, X., and Li, L. (2011). Data-driven multi-touch attribution models. Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA.

2006–2010

• Li, L., Li, B., and Zhu, L.X. (2010). Groupwise dimension reduction. Journal of the American Statistical Association, 105, 1188-1201.

• Li, L.. (2010). Dimension reduction for high dimensional data. Book chapter in Statistical Methods in Molecular Biology, Ed. Bang, H., Zhou, X., Van Epps, H.L. and Mazumdar, M. Humana Press.

• Cai, Y., Chow, M.Y., Lu, W., and Li, L. (2010). Statistical feature selection from massive data in distribution fault diagnosis. IEEE Transactions on Power Systems, 25, 642-648.

• Cai, Y., Chow, M.Y., Lu, W., and Li, L. (2010). Evaluation of distribution fault diagnosis algorithms using ROC curves. Proceedings of Power and Energy Society General Meeting, Minneapolis, MN.

• Cook, R.D., and Li, L. (2009). Dimension reduction in regressions with exponential family predictors. Journal of Computational and Graphical Statistics, 18, 774-791.

• Setodji, C.M., and Li, L. (2009). Model free multivariate reduced-rank regression with categorical predictors. Statistica Sinica, 19, 1119-1136.

• Li, L., and Yin, X. (2009). Longitudinal data analysis using sufficient dimension reduction method. Computational Statistics and Data Analysis, 53, 4106-4115.

• Li, L. (2009). Exploiting predictor domain information in sufficient dimension reduction. Computational Statistics and Data Analysis, 53, 2665-2672.

• Bondell, H.D., and Li, L. (2009). Shrinkage inverse regression estimation for model free variable selection. Journal of the Royal Statistical Society, Series B., 71, 287-299.

• Cornish, K.M., Kogan, C.S., Li, L., Turk, J., Jacquemont, S., and Hagerman, R.J. (2009). Lifespan changes in working memory in fragile X premutation males. Brain and Cognition, 69, 551-558.

• Lu, W., and Li, L. (2008). Boosting methods for nonlinear transformation models with censored survival data. Biostatistics, 9, 658-677.

• Li, L., and Tsai, C.L. (2008). Constrained regression model selection. Journal of Statistical Planning and Inference, 138, 3939-3949.

• Li, L., and Yin, X. (2008). Rejoinder to “A note on sliced inverse regression with regularizations”. Biometrics, 64, 984-986.

• Li, L., and Lu,W. (2008). Sufficient dimension reduction with missing predictors. Journal of the American Statistical Association, 103, 822-831.

• Li, L. (2008). Comments on “Augmenting the bootstrap to analyze high dimensional genomic data” by S. Tyekucheva and F. Chiaromonte. Test, 17, 22-24.

• Li, L., and Yin, X. (2008). Sliced inverse regression with regularizations. Biometrics, 64, 124-131.

• Leehey, M.A., Berry-Kravis, E., Goetz, C.G., Zhang, L., Hall, D.A., Li, L., Rice, C.D., Lara, R., Cogswell, J., Reynolds, A., Gane, L., Jacquemont, S., Tassone, F., Grigsby, J., Hagerman, R.J., and Hagerman, P.J. (2008). FMR1 CGG repeat length predicts motor dysfunction in premutation carriers. Neurology, 70, 1397-1402.

• Cornish, K.M., Li, L., Kogan, C.S., Jacquemont, S., Turk, J., Dalton, A., Hagerman, R.J., and Hagerman, P.J. (2008). Age-dependent cognitive changes in carriers of the Fragile X Syndrome. Cortex, 44, 628-636.

• Li, L. (2007). Sparse sufficient dimension reduction. Biometrika, 94, 603-613.

• Li, L., Cook, R.D., and Tsai, C.L. (2007). Partial inverse regression method. Biometrika, 94, 615-625.

• Li, L., and Nachtsheim, C.J. (2007). Comment on “Fisher lecture: dimension reduction in regression” by R.D. Cook. Statistical Science, 22, 36-39.

• Li, L., Simonoff, J.S., and Tsai, C.L. (2007). Tobit model estimation and sliced inverse regression. Statistical Modelling, 7, 107-123.

• Tassone, F., Beilina, A., Carosi, C., Albertosi, S., Bagni, C., Li, L., Glover, K., Bentley, D., and Hagerman, P.J. (2007). Elevated FMR1 mRNA in premutation carriers is due to increased transcription. RNA, 13, 555-562.

• Tassone, F., Adams, J., Berry-Kravis, E.M., Cohen, S.S., Brusco, A., Leehey, M.A., Li, L., Hagerman, R.J., and Hagerman, P.J. (2007). CGG correlates with age of onset of motor signs of the Fragile X-associated TremorAtaxia Syndrome (FXTAS). American Journal of Medical Genetics, Part B: Neuropsychiatric Genetics/, 144, 566-569.

• Berry-Kravis, E., Goetz, C., Leehey, M.A., Hagerman, R.J., Zhang, L., Li, L., Nguyen, D., Hall, D.A., Tartaglia, N., Cogswell, J., Tassone, F., and Hagerman, P.J. (2007). Neuropathic features in fragile X premutation carriers. American Journal of Medical Genetics, Part A., 143, 19-26.

• Li, L., and Nachtsheim, C.J. (2006). Sparse sliced inverse regression. Technometrics, 48, 503-510.

• Azari, R., Li, L., and Tsai, C.L. (2006). Longitudinal data model selection. Computational Statistics and Data Analysis, 50, 3053-3066.

• Li, L. (2006). Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information. Bioinformatics, 22, 466-471.

2005 and earlier

• Li, L., Cook, R.D., and Nachtsheim, C.J. (2005). Model-free variable selection. Journal of the Royal Statistical Society, Series B., 67, 285-299.

• Li, L., and Li, W. (2005). Tabu search and perturbation methods in the construction of supersaturated designs. American Journal of Mathematical and Management Sciences, 25, 189-205.

• Li, L., Cook, R.D., and Nachtsheim, C.J. (2004). Cluster-based estimation for sufficient dimension reduction. Computational Statistics and Data Analysis, 47, 175-193.

• Li, L., and Li, H. (2004). Dimension reduction methods for microarrays with application to censored survival data. Bioinformatics, 20, 3406-3412.

• Li, L., and Nachtsheim, C.J. (2004). Discussion of “A goodness-of-fit test for single-index models” by Y. Xia, et al. Statistica Sinica, 14, 28-34.

• Cook, R.D., and Li, L. (2003). Discussion of “The focused information criterion” by G. Claeskens and N.L. Hjort. Journal of the American Statistical Association, 98, 925-928.

• Li, L. (2002). Comment on “An adaptive estimation of dimension reduction space” by Y. Xia, et al. Journal of the Royal Statistical Society, Series B., 64, 399-400.