Selected Journal Articles
Model Assessment
- 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]
- 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]
- 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];
- Wu, T., Feng, C., Li, L. (2024). A Comparison of Estimation Methods for Shared Gamma Frailty Models. Statistics in Biosciences 0, 1–22. [PDF]
- 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].
- 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.
- 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].
- 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
- Yin, W., Li, L., Wu, F.-X. (2022). Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing 469, 332–345.
- 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.
- 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.
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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].
- Yao, W. and Li, L. (2014). A New Regression Model: Modal Linear Regression. Scandinavian Journal of Statistics, 41(3), 656-671. [PDF].
- 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].
- 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].
- 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].
- Li, L. (2010). Are Bayesian Inferences Weak for Wasserman's Example? Communications in Statistics -- Computation and Simulation, 2010, 39(3), 655-667. [PDF]; [slides].
- 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:
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Software and Apps
Online Apps
- Students' Grade Calculator: A Shinylive app that can calculate students' grades with fine-grained controls and output.
- Animation of Finding $\sqrt{S}$: A Shinylive App for Finding Square Root using Newton Method
- Latex Abbreviation Extractor: A streamlit (python-based) app that can extract abbreviations in the latex source text.
- Real-time estimates of the reproduction rate (Rt) of Canada and its provinces, a website maintained until Feb 2022.
- 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.
- Z-residual: Computing Z-residual for survival models and generalized linear models. (under development) A demonstration for checking covariate functional forms, [GitHub].
-
Z-residual for survival models: R Functions for Computing Z-residuals for
survreg
andcoxph
Objects. - HTLR: Bayesian Logistic Regression with Hyper-LASSO priors, [CRAN], [Github].
- iIS: R code for computing iIS predictive p-values in disease mapping models.
- BCBCSF: Bias-corrected Bayesian Classification with Selected Features, [CRAN].
- ARS: C Function for Adaptive Rejection Sampling (ARS).
- BPHO: Bayesian prediction with high-order interactions, [CRAN].
- gibbs.met: Naive Gibbs Sampling with Metropolis Steps, [CRAN].
- predbayescor: Classification rule based on Bayesian naive Bayes models with feature selection bias corrected, [CRAN].
- predmixcor: Classification rule based on Bayesian mixture models with feature selection bias corrected, [CRAN].