2011:

[46]

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. In press.

[45]

Li, B., Artemiou, A., and Li, L. (2011). Principal support vector machines for linear and nonlinear sufficient dimension reduction. The Annals of Statistics. In press.

[44]

Reich, B.J., Bondell, H.D., and Li, L. (2011). Sufficient dimension reduction via Bayesian mixture modeling. Biometrics. In press.

[43]

Sun, W., and Li, L. (2011). Multiple loci mapping via model-free variable selection. Biometrics. In press

[42]

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

[41]

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

[40]

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.

[39]

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

[38]

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. In press.

2010:.

[37]

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

[36]

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

[35]

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.

[34]

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, 1-6.

2009:.

[33]

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.

[32]

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

[31]

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

[30]

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

[29]

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

[28]

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.

2008:.

[27]

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

[26]

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

[25]

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

[24]

Lu, W., and Li, L. (2008). Boosting methods for nonlinear transformation models with censored survival data. Biostatistics. 9, 658-667.

[23]

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

[22]

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

[21]

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

[20]

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.

2007:.

[19]

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

[18]

Li, L., Cook, R.D., and Tsai, C.L. (2007). Partial inverse regression. Biometrika. 94,

615-625.

[17]

Li, L., and Nachtsheim, C.J. (2007). Comment: Fisher Lecture: Dimension Reduction in Regression by R. D. Cook. Statistical Science. 22, 36-39.

[16]

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

[15]

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

[14]

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

[13]

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., Hagerman, P.J. (2007). Neuropathic features in fragile X premutation carriers. American Journal of Medical Genetics, Part A. 143, 19-26.

2006:.

[12]

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

[11]

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

[10]

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

2005:.

[9]

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.

[8]

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.

[7]

Li, L., and Li, H. (2005). A local polynomial method for detection of gene copy number changes in human cancer. Proceedings of the Joint Statistical Meetings, Minneapolis, MN

2004 and earlier:.

[6]

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.

[5]

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

[4]

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.

[3]

Li, L., Cook, R.D., and Nachtsheim, C.J. (2004). SIR3: dimension reduction in the presence of linearly or nonlinearly related predictors. Institute of Statistics Mimeo Series 2607.

[2]

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.

[1]

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.