Research Interest

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

Publications

Find my pulications from: zotero profile (self maintained with PDF files), google scholar, researchgate, web of science, semantic scholar, arXiv, or my publication page.

Selected Papers and R Packages

• Z-residual, An R package for computing Z-residual for survival models and generalized linear models. A demonstration for checking the functional forms of covariates, Github page for downloading the package.

• HTLR: An R package for Fitting 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.

• R functions for computing NRSP residuals for survreg and coxph objects with animated demonstration

• Li, L., Wu, T., Feng, C. (2021) Model Diagnostics for Censored Regression via Randomized Survival Probabilities. Statistics in Medicine. 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. Jun 16;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-I], [slides-II], Statistics and Computing, Volume 26, Issue 4, pp 881-897.

• Yao, W. and Li, L. (2014). A New Regression Model: Modal Linear Regression. Scandinavian Journal of Statistics,Volume 41, Issue 3, pages 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. [technical report], [software], [slides].