Software

Neuroimaging analysis

• Code for dynamic tensor clustering

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  • Reference: Sun, W.W. and Li, L. (2018+). Dynamic tensor clustering. Journal of the American Statistical Association, in press.

• Code for two-sample matrix graph test

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  • Reference: Xia, Y. and Li, L. (2018+). Matrix graph hypothesis testing and application in brain connectivity alternation detection. Statistica Sinica, in press.

• Code for multiple matrix graphs estimation

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

• Code for sparse tensor response regression

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

• Code for one-sample matrix graph test

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

• Code for tensor response envelope regression

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

• Code for tensor envelope partial least squares

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

• Code for tensor predictor regression

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

  • Reference: 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.

Dimension reduction and variable selection

• Code for Bayesian sufficient dimension reduction

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

• Code for groupwise sufficient dimension reduction

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

• Code for sufficient dimension reduction with exponential family predictors

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

• Code for shrinkage inverse regression estimation for model-free variable selection

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  • Reference: 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.

• Code for regularized sliced inverse regression

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

• Code for sparse sufficient dimension reduction

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

• Code for sufficient dimension reduction of censored survival data

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