Research Interest

Statistical machine learning, survival analysis, model checking, residual diagnostics

Publications

Find my pulications from
                   

Selected Papers and R Packages

• Z-residual: Computing Z-residual for survival models and generalized linear models. (under development) A demonstration for checking covariate functional forms, Github page.
• HTLR: Fitting Bayesian Logistic Regression with Heavy-tailed (Hyper-Lasso) Priors, [CRAN page], [Github page].
• NRSP: Computing NRSP residuals (Z-residuals) for survreg and coxph objects, the page for downloading
• Li, L., Wu, T., Feng, C. (2021). Model diagnostics for censored regression via randomized survival probabilities. Statistics in Medicine, 40:6, 1482-1497. arxiv reprint (accepted version);Slides.
• Feng C, Li L, Sadeghpour A. (2020). A comparison of residual diagnosis tools for diagnosing regression models for count data. BMC Medical Research Methodology. 20:1, 175.
• Dong M, Li L, Chen M, Kusalik A, Xu W. (2020). Predictive analysis methods for human microbiome data with application to Parkinson's disease. PLOS ONE, 15:8, e0237779.
• Jiang L, Greenwood CMT, Yao W, Li L. (2020). Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data. Scientific Reports, 10:1,9747.
• Li, L., Qiu, S., Zhang, B., and Feng, C.X. (2016). Approximating Cross-validatory Predictive Evaluation in Bayesian Latent Variables Models with Integrated IS and WAIC. arXiv 1404.2918, [slides], Statistics and Computing, 26:4, 881-897.
• Yao, W. and Li, L. (2014). A New Regression Model: Modal Linear Regression. Scandinavian Journal of Statistics, 41:3, 656-671. [technical report].
• Li, L. (2012). Bias-corrected Hierarchical Bayesian Classification with a Selected Subset of High-dimensional Features. Journal of American Statistical Association, 107:497, 120-134. [accepted version], [software], [slides].