Selected Journal Articles

Model Assessment

  1. Wu, T., Feng, C., Li, L. (2024). Cross-validatory Z-Residual for Diagnosing Shared Frailty Models. The American Statistician 79(2), 198–211. [PDF]; [Free Reprints]; [slides]; [Z-residual on Github]; [Demo]
  2. Wu, T., Li, L., Feng, C. (2024). Z-residual diagnostic tool for assessing covariate functional form in shared frailty models. Journal of Applied Statistics 52(1), 28–58. [PDF]; [Z-residual on Github]; [Demo] [slides]
  3. Li, L., Wu, T., Feng, C. (2021). Model diagnostics for censored regression via randomized survival probabilities. Statistics in Medicine 40, 1482–1497. [PDF]; [R Functions and Demonstration]; [slides];
  4. Wu, T., Feng, C., Li, L. (2024). A Comparison of Estimation Methods for Shared Gamma Frailty Models. Statistics in Biosciences 0, 1–22. [PDF]
  5. 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].
  6. 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.
  7. Li, L., Feng, C., and Qiu, S. (2017), Estimating Cross-validatory Predictive P-values with Integrated Importance Sampling for Disease Mapping Models, Statistics in Medicine, 36(14), 220-2236. [PDF]; [slides]; [R Functions].
  8. 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, Statistics and Computing, 26(4), 881-897. [PDF]; [slides].

Statistical Machine Learning

  1. Yin, W., Li, L., Wu, F.-X. (2022). Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 469, 332–345.
  2. 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.
  3. 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.
  4. Shi, J., et al. (2019). Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection. BMC Bioinformatics, 20(15):1–14.
  5. Li, L. and Yao, W. (2018). Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection. Journal of Statistical Computation and Simulation, 88:14, 2827-2851. [PDF]; [software]; [slides].
  6. Yao, W. and Li, L. (2014). A New Regression Model: Modal Linear Regression. Scandinavian Journal of Statistics, 41(3), 656-671. [PDF].
  7. 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. [PDF]; [software]; [slides].
  8. Li, L., and Neal, R. M. (2008). Compressing Parameters in Bayesian High-order Models with Application to Logistic Sequence Models. Bayesian Analysis, 2008, 3(4), 793-822. [PDF]; [slides]; [software].
  9. Li, L., Zhang, J., and Neal, R. M. (2008). A Method for Avoiding Bias from Feature Selection with Application to Naive Bayes Classification Models. Bayesian Analysis, 2008, 3(1), 171-196. [PDF]; [slides]; [software].
Others
  1. Li, L. (2010). Are Bayesian Inferences Weak for Wasserman's Example? Communications in Statistics -- Computation and Simulation, 2010, 39(3), 655-667. [PDF]; [slides].
  2. Soltanifar, M., Li, L., and Rosenthal, J. S. (2010). A Collection of Exercises in Advanced Probability Theory, World Scientific Publishing, Singapore. [PDF].

All Publications

My other publications are available from the following public archives:

Software and Apps

Online Apps

  1. Students' Grade Calculator: A Shinylive app that can calculate students' grades with fine-grained controls and output.
  2. Animation of Finding $\sqrt{S}$: A Shinylive App for Finding Square Root using Newton Method

  3. Latex Abbreviation Extractor: A streamlit (python-based) app that can extract abbreviations in the latex source text.
  4. Real-time estimates of the reproduction rate (Rt) of Canada and its provinces, a website maintained until Feb 2022.
  5. Projections of Canada's COVID-19 Cases (up to 2020-06-30)

Software

To use the following software correctly, you may need to read relevant technical reports (research papers) listed on my publications page.

  1. Z-residual: Computing Z-residual for survival models and generalized linear models. (under development) A demonstration for checking covariate functional forms, [GitHub].
  2. Z-residual for survival models: R Functions for Computing Z-residuals for survreg and coxph Objects.
  3. HTLR: Bayesian Logistic Regression with Hyper-LASSO priors, [CRAN], [Github].
  4. iIS: R code for computing iIS predictive p-values in disease mapping models.
  5. BCBCSF: Bias-corrected Bayesian Classification with Selected Features, [CRAN].
  6. ARS: C Function for Adaptive Rejection Sampling (ARS).
  7. BPHO: Bayesian prediction with high-order interactions, [CRAN].
  8. gibbs.met: Naive Gibbs Sampling with Metropolis Steps, [CRAN].
  9. predbayescor: Classification rule based on Bayesian naive Bayes models with feature selection bias corrected, [CRAN].
  10. predmixcor: Classification rule based on Bayesian mixture models with feature selection bias corrected, [CRAN].