Publications of Longhai Li and His Team
Real-time estimates of the reproduction rate (Rt) of Canadian provinces: https://longhaisk.github.io/CanadaCovidRt/
- 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. Dagasso, G., Yan, Y., Wang, L., Li, L., Kutcher, R., Zhang, W., Jin, L., 2020. Comprehensive-GWAS: a pipeline for genome-wide association studies utilizing cross-validation to assess the predictivity of genetic variations, in: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1361–1367. Yin, W., Li, L., Wu, F.-X., 2020. Deep learning for brain disorder diagnosis based on fMRI images. Neurocomputing.
- 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.
- Projections of Canada's COVID-19 Cases (up to 2020-06-30)
- Shi J, Yan Y, Links MG, Li L, Dillon J-AR, Horsch M, et al. Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection. BMC bioinformatics. 2019;20(15):1–14.
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, arXiv:1405.3319, [software], [slides].
Jin, L., McQuillan, I., Li, L. (2017). Computational identification of harmful mutation regions to the activity of transposable elements. BMC Genomics 18, 862.
Cindy X. Feng, Mehdi Rostami, and Longhai Li (2017), Impact of Misspecified Residual Correlation Structure on the Parameter Estimates in a Shared Spatial Frailty Model. Journal of Statistical Computation and Simulation, 87 (12), 2384-2410.
Longhai Li, Cindy X. Feng, and Shi Qiu (2017), Estimating Cross-validatory Predictive P-values with Integrated Importance Sampling for Disease Mapping Models. [slides], [R code], arXiv:1603.07668, Statistics in Medicine, Volume 36, Issue 14, Pages 2220-2236.
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.
Jin, McQuillan, and Li (2016). Computational Identification of Regions that Influence Activity of Transposable Elements in the Human Genome, accepted as a regular paper by the 2016 IEEE International Conference on Bioinformatics and Biomedicine (acceptance rate for regular papers 0.19).
Feng, C. X. and Li, L. (2016). Modeling Zero Inflation and Overdispersion in the Length of Hospital Stay for Patients with Ischaemic Heart Disease, in the book Advanced Statistical Methods in Big-Data Sciences, edited by D. Chen, J. Chen J, X. Lu, G. Yi and H. Yu, Springer.
Longhai Li (2014), An Introduction to Microarray Data, Invited talk for the workshop Statistical Issues in Biomarker and Drug Co-development, Fields Institute, Toronto. [video].
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].
Yao, W. and Li, L. (2014). An Online Bayesian Mixture Labelling Method by Minimizing Deviance of Classification Probabilities to Reference Labels. Journal of Statistical Computation and Simulation, 84 (2), 310-323.
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].
Khan, S. A., Rana, M., Li, L., Dubin, J. A., (2012). A Statistical Investigation to Monitor and Understand Atmospheric CFC Decline with the Spatial-Longitudinal Bent-Cable Model. International Journal of Statistics and Probability, 1(2), 56-68. [technical report].
Sajobi, T. T., Lix, L. M., Dansu, B.M., Laverty, W., Li, L. (2012). Robust Descriptive Discriminant Analysis for Repeated Measures Data. Computational Statistics and Data Analysis, 56 (9), 2782-2794.
Sajobi, T.T., Lix, L. M., Li, L., and Laverty, W. (2011). Discriminant Analysis for Repeated Measures Data: Effects of Mean and Covariance Misspecification on Bias and Error in Discriminant Function Coefficients. Journal of Modern Applied Statistical Methods, 10 (2), 571-582. [technical report]
Li, L. (2010). Are Bayesian Inferences Weak for Wasserman's Example? Communications in Statistics -- Computation and Simulation, 2010, 39(3), 655-667. [technical report], [slides].
Soltanifar, M., Li, L., and Rosenthal, J. S. (2010). A Collection of Exercises in Advanced Probability Theory, World Scientific Publishing, Singapore.
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. [technical report], [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. [technical report], [slides], [software].
Li, L. (2007). Bayesian Classification and Regression with High Dimensional Features, Ph.D. thesis, University of Toronto. [software for: Chapter 2.1, Chapter 2.2, Chapter 3].
Li, L., Du, M. and Jin, Z. (2006). Analysis of Obstructive Sleep Apnea (OSA) Data with Bayesian Neural Network, an oral presentation for the case study contest held by Statistical Society of Canada at London Canada. The results of predicting OSA are pretty interesting.
Li, L. (2006), A New Proof of Peskun's and Tierney's Theorems using Matrix Method, An Invited talk for Graduate Student's Seminar, Department of Statistics, University of Toronto.
Li, L. (2004), Calculating the Confidence Intervals Using Bootstrap, an oral talk for an internal project meeting of a research group studying safety of nuclear power, November 2004, Ontario Power Generation, Toronto, Canada.