# Publications and Software

**Software
**

- A suite of code for covariance modelling in longitudinal data, including an implementation of the method in Zhang, Leng, and Tang (JRSSB, 2015), can be found here.
- R package (version 2.0) and manual for MIP (multiple influence point detection in high-dimensional spaces, JRSSB, 2019), see https://arxiv.org/abs/1609.03320. After installing the package, try R command "example(MIP)".
- R Code for HOLP in screening variables, see Wang and Leng (JRSSB, 2016).
- Matlab Code and an Example for estimating high-dimensional correlation matrices. See Cui, Leng, and Sun (CSDA, 2015, Sparse estimation of high-dimensional correlation matrices).
- Matlab Code (zip file) for gradient-based kernel dimension reduction in Fukumizu and Leng (JASA, 2014).
- Matlab Code (rar file) for Bayesian adaptive Lasso. See Leng, Tran, and Nott (AISM, 2014, Bayesian adaptive lasso).
- R Code and an Example for sparse matrix graphical models in Leng and Tang (JASA, 2012).
- R Code (rar file) and an Example for penalised empirical likelihood in Tang and Leng (Biometrika, 2010) and Leng and Tang (Biometrika, 2012).
- Matlab Code (rar file) for predictive Lasso in Tran, Nott, and Leng (STCO, 2012, The predictive lasso).
- R Code (rar file) for variable selection in heteroscedastic linear models. See Nott, Tran, and Leng (STCO, 2012, Variational approximation for heteroscedastic linear models and matching pursuit algorithms).
- R Code and an Example for regularised rank regression in Leng (Statistica Sinica, 2010, Variable selection and coefficient estimation via regularized rank regression).
- R Code and an Example for sparse PCA in Leng and Wang (JCGS, 2009, On general adaptive sparse principal component analysis).
- R Code and an Example for variable selection via least squares approximation in Wang and Leng (JASA, 2007).

**Selected Publications (**More can be found on Google Scholar**)**

**in journals**

- Wang, Y., Xu, H., and Leng, C. (2019). Provable subspace clustering: When LRR meets SSC. IEEE Transactions on Information Theory, to appear.
- Tang, C.Y., Zhang, W., and Leng, C. (2019). Discrete longitudinal data modeling with a mean-correlation regression approach. Statistica Sinica. 29, 853-876.
- Zhao, J., Liu, C., Niu, L., and Leng, C. (2019). Multiple influential point detection in high-dimensional spaces. Journal of the Royal Statistical Society Series B, 81, 385-408. The journal version can be found here (
**open access**!). - Jiang, B., Wang, X., and Leng, C. (2018). A direct approach for sparse quadratic discriminant analysis. Journal of Machine Learning Research, 19, 1-37.
- Yan, T., Jiang, B., Fienberg, S. E., and Leng, C. (2018). Statistical inference in a directed network model with covariates. Journal of the American Statistical Association, to appear.
- Leng, C. and Pan, G. (2018). Covariance estimation via sparse Kronecker structures. Bernoulli, 24, 3833-3863.
- Chen, Z. and Leng, C. (2016). Dynamic covariance models. Journal of the American Statistical Association, 111, 1196-1207. Supplementary materials.
- Wang, X. and Leng, C. (2016). High-dimensional ordinary least-squares projection for screening variables (http://arxiv.org/abs/1506.01782). Journal of the Royal Statistical Society Series B, 78, 589-611.
- Leng, C. and Yan, T. (2016). Discussion of "Statistical modelling of citation exchange between statistics journals" by Varin, Cattelan and Firth, Journal of the Royal Statistical Society Series A, 179, 54.
- Zhao, J. and Leng, C. (2016). An analysis of penalised interaction models. Bernoulli, 22, 1937-1961.
- Yan, T., Leng, C., and Zhu, J. (2016). Asymptotics in directed exponential random graph models with an increasing bi-degree sequence (http://arxiv.org/abs/1408.1156). The Annals of Statistics, 44, 31-57.
- Yan, T. and Leng, C. (2015). A simulation study of the p1 model for directed random graphs. Statistics and Its Interface, 8, 255-266.
- Zhang, W., Leng, C., and Tang, C. Y. (2015). A joint modeling approach for longitudinal studies (pdf). Journal of the Royal Statistical Society Series B, 77, 219-238. This video explains the geometric interpretation of the angles in a new variance-correlation decomposition. (Authors' note: The angles are denoted as β's in the video instead of φ's as in the paper for better visualization due to technical reasons.)
- Fukumizu, K. and Leng, C. (2014). Gradient-based kernel dimension reduction (pdf). Journal of the American Statistical Association, 109, 359-370.
- Zhao, J., Leng, C., Li, L., and Wang, H. (2013). High dimensional influence measure. The Annals of Statistics, 41, 2639-2667.
- Leng, C. and Tong, X. (2013). A quantile regression estimator for censored data (http://arxiv.org/abs/1302.0181). Bernoulli, 19, 344-361.
- Leng, C. and Tang, C. Y. (2012). Sparse matrix graphical models. Journal of the American Statistical Association, 107, 1187-1200.
- Leng, C. and Tang, C. Y. (2012). Penalized empirical likelihood and growing dimensional general estimating equations. Biometrika, 99, 703-716.
- Zhang, W. and Leng, C. (2012). A moving average Cholesky factor model in covariance modeling for longitudinal data. Biometrika, 99, 141-150.
- Tang, C. Y. and Leng, C. (2011). Empirical likelihood and quantile regression in longitudinal data analysis. Biometrika, 98, 1001-1006.
- Leng, C. and Li, B. (2011). Forward adaptive banding for estimating large covariance matrices. Biometrika, 98, 821-830.
- Tang, C. Y. and Leng, C. (2010). Penalized high dimensional empirical likelihood. Biometrika, 97, 905-920.
- Leng, C., Zhang, W., and Pan, J. (2010). Semiparametric mean-covariance regression analysis for longitudinal data. Journal of the American Statistical Association, 105, 181-193.
- Wang, H., Li, B., and Leng, C. (2009). Shrinkage tuning parameter selection with a diverging number of parameters. Journal of the Royal Statistical Society, Series B, 71, 671-683.
- Leng, C. and Wang, H. (2009). On general adaptive sparse principal component analysis. Journal of Computational and Graphical Statistics, 18, 201-215.
- Wang, H. and Leng, C. (2007). Unified Lasso estimation via least squares approximation. Journal of the American Statistical Association, 102, 1039-1048.
- Leng, C., Lin, Y., and Wahba, G. (2006). A note on the Lasso and related procedures in model selection. Statistica Sinica, 16, 1273-1284.

**in conferences**

- Wang, X., Dunson, D., and Leng, C. (2016). No penalty no tears: Least squares in high-dimensional linear models. ICML.
- Wang, X., Leng, C., and Dunson, D. (2015). On the consistency theory of high dimensional variable screening (http://arxiv.org/abs/1502.06895). NIPS.
- Zhu, C., Xu, H., Leng, C., and Yan, S. (2014). Convex optimization procedure for clustering: Theoretical revisit. NIPS.
- Wang, Y., Xu, H., and Leng, C. (2013). Provable subspace clustering: When LRR meets SSC. NIPS. Spotlight presentation. See also Yuxiang's website for code.
- Fukumizu, K. and Leng, C. (2012). Gradient-based kernel method for feature extraction and variable selection. NIPS.
- Xu, H. and Leng, C. (2012). Robust multi-task regression with grossly corrupted observations. AISTATS.