Research Interest

Statistical machine learning, survival analysis, model checking, residual diagnostics, model comparison, cross-validation, information criterion, zero-inflated models, high-throughput data, and mixed-effects models.

Publications

See my zotero profile page (self maintained with PDF files), google scholar page, researchgate page, web of science page, or my publication page.

Highlights and News

• Bai, W., Dong, M., Li, L., Feng, C., Xu, W., 2021. Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data. BMC Bioinformatics 22, 564; Slides for this paper.
• Li, L., Wu, T., Feng, C. Model Diagnostics for Censored Regression via Randomized Survival Probabilities. Statistics in Medicine, 2021. arxiv reprint (accepted version) ; R functions for computing NRSP residuals for survreg and coxph objects with animated demonstration; Slides.
• Feng C, Li L, Sadeghpour A. A comparison of residual diagnosis tools for diagnosing regression models for count data. BMC Medical Research Methodology. 2020 Jul 1;20(1):175.
• Dong M, Li L, Chen M, Kusalik A, Xu W. Predictive analysis methods for human microbiome data with application to Parkinson's disease. PLOS ONE. 2020 Aug 24;15(8):e0237779.
• Jiang L, Greenwood CMT, Yao W, Li L. Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data. Scientific Reports. 2020 Jun 16;10(1):9747.
• HTLR: Bayesian Logistic Regression with Heavy-Tailed Priors, [CRAN page], [Github page]. HTLR was listed in the top 40 new packages in October 2019 by R views of Rstudio.