Jialiang Li

Professor
Department of Statistics & Data Science
National University of Singapore
E-mail: jialiang at nus.edu.sg
Phone: (65)6516-8932
Office: S16-07-111

Adjunct Appointment

Duke University-NUS Graduate Medical School

Current Interests

Change point; Diagnostic medicine; Instrumental variable; Network; Personalized medicine; Smoothing; Statistical learning; Survival analysis.

Honours and Awards

  • Fellow of American Statistical Association (ASA).
  • Fellow of Institute of Mathematical Statistics (IMS).
  • Elected Member of International Statistical Institute (ISI).

Recent Works

  • Liu, Y, Luo, S, Li, J. (2024). Hypothesis tests in ordinal predictive models with optimal accuracy. Biometrics. Accepted.
  • Ding, J., Li, J., Wang, X. (2024). Efficient auxiliary information synthesis for cure rate model. Journal of the Royal Statistical Society: Series C. 73(2):497-521.
  • Ding, J., Li, J., Wang, X. (2024). Renewable risk assessment of heterogeneous streaming time-to-event cohorts. Statistics in Medicine. Accepted.
  • Feng, Q., Liu, P., Kuan, P.F., Zou, F., Chen, J., Li, J. (2023). A network approach to compute hypervolume under ROC manifold for multi-class biomarkers. Statistics in Medicine. 42(6): 834-859.
  • Seng, L.L., Liu, C.T., Wang, J., Li, J. (2023). Instrumental Variable Model Average with Applications in Mendelian Randomization. Statistics in Medicine. 42(9):3547-3567.
  • Jiang, B., Li, J., Yao, Q. (2023). Autoregressive Networks. Journal of Machine Learning Research. 24(227):1-69.
  • Ding, J., Li, J., Han, Y., McKeague, I.W., Wang, X. (2023). Fitting additive risk models using auxiliary information. Statistics in Medicine. 42(6): 894-916.
  • Geng, Z., Li, J., Niu, Y., Wang, X. (2023). Goodness-of-fit test for a parametric mixture cure model with partly interval-censored data. Statistics in Medicine. 42(4): 407-421.
  • Wang, B., Li, J., Wang, X. (2022). Multi-threshold Proportional Hazards Model and Subgroup Identification. Statistics in Medicine. 41(29):5715-5737.
  • Li, J., Li, Y., Hsing, T. (2022). On Functional Processes with Multiple Discontinuities. Journal of the Royal Statistical Society Series B. 84(3): 933-972.
  • Li, J., Lv, J., Wan, A.K.T., Liao, J. (2022). AdaBoost semiparametric model averaging prediction for multiple categories. Journal of the American Statistical Association (T&M). 117: 495-509.
  • Wang, J. [PhD student], Li, J. (2022). Multi-threshold Structural Equation Model. Journal of Business & Economic Statistics. Accepted.
  • Seng, L. [PhD Student], Li, J. (2022). Structural Equation Model Averaging: Methodology and Application. Journal of Business & Economic Statistics. 40(2): 815-828.
  • Fang, F., Li, J., Xia, X. (2022). Semiparametric Model Averaging Prediction for Dichotomous Response. The Journal of Econometrics. 229(2): 219-245.
  • Guo, C., Li, J. (2022). Homogeneity and structure identification in semiparametric factor models. Journal of Business & Economic Statistics. 40(1), 408-422.
  • Li, J., Yu, T., Lv, J. Lee, M.L.T. (2021). Semiparametric Model Averaging Prediction for Lifetime Data via Hazards Regression. Journal of the Royal Statistical Society Series C. 70(5): 1187-1209.
  • Li, J., Li, Y., Jin, B., Kosorok, M.R. (2021). Multi-threshold Change Plane Model: Estimation Theory and Applications in Subgroup Identification. Statistics in Medicine. 40(15): 3440-3459.
  • Sande, S.Z. [PhD student], Li, J., D’Agostino, R., Wong, T.Y., Cheng, C.Y. (2020). Statistical Inference for Decision Curve Analysis, with Applications to Cataract Diagnosis. Statistics in Medicine. 39(22): 2980-3002.
  • Li, J., Gao, M., D’Agostino, R. (2019). Evaluating Classification Accuracy for Modern Learning Approaches. Statistics in Medicine (Tutorials in Biostatistics). 38(13): 2477-2503.
  • Wang, J. [PhD student], Li, J., Li, Y., Wong, W.K. (2019). A model-based multi-threshold method for subgroup identification. Statistics in Medicine. 38(14): 2605-2631.
  • Li, J., Yue, M., Zhang, W. (2019). Subgroup Identification via Homogeneity Pursuit for Dense Longitudinal/Spatial Data. Statistics in Medicine. 38(17): 3256-3271.
  • Li, J., Xia, X., Wong, W.K., Nott, D. (2018). Varying coefficient semiparametric model average prediction. Biometrics. 74, 1417–1426.
  • Li, J., Jin, B. (2018). Multi-threshold Accelerated Failure Time Model. The Annals of Statistics. 46: 2657-2682.
  • Li, J., Zhang, W., Kong, E. (2018). Factor Models for Asset Returns Based on Transformed Factors. The Journal of Econometrics. 207: 432-448.
  • Yue, M. [PhD Student], Li, J., Ma, S. (2018). Sparse Boosting for High-Dimensional Survival Data with Varying Coefficients. Statistics in Medicine. 37(5): 789-800.
  • Li, J., Huang, C., Zhu, H. (2017). A Functional Varying-Coefficient Single Index Model for Functional Response Data. Journal of the American Statistical Association (T&M). 112: 1169-1181.
  • Cheng, M., Honda, T., Li, J. (2016). Efficient estimation in semivarying coefficient models for longitudinal/clustered data. The Annals of Statistics. 44(5): 1988-2017.
  • Ke, Y., Li, J., Zhang, W. (2016). Structure Identification in Panel Data Analysis. The Annals of Statistics. 44(3): 1193-1233.
  • Huang, Z. [PhD student], Li, J., Cheng, C.Y., Cheung, C., Wong, T.Y. (2016). Bayesian reclassification statistics for assessing improvements in diagnostic accuracy. Statistics in Medicine. 35(15): 2574-2592.
  • Li, J., Zheng, Q., Peng, L., Huang, Z. (2016). Survival impact index and ultrahigh-dimensional model-free screening with survival outcomes. Biometrics. 72(4): 1145-1154.
  • Li, J., Fine, J. and Brookhart, A. (2015). Instrumental variable additive hazards models. Biometrics. 71: 122-130.
  • Shao, F. [PhD student], Li, J., Ma, S. and Lee, M.-L.T. (2014). Semiparametric Varying-coefficient Model for Interval Censored Data with a Cured proportion. Statistics in Medicine. 33(10): 1700—1712.
  • Salim, A., Ma, X., Li, J., Reilly, M. (2014). A maximum likelihood method for secondary analysis of nested case-control data. Statistics in Medicine. 33(11):1842-52.
  • Kuk, A. Y. C., Li, J. and Rush, J. A. (2014). Variable and threshold selection to control predictive accuracy in logistic regression. Journal of the Royal Statistical Society Series C (Applied Statistics). 63:657-672.
  • Cheng, M.Y., Honda, T., Li, J., Peng, H. (2014). Nonparametric independence screening and structural identification for ultra-high dimensional longitudinal data. The Annals of Statistics. 42(5): 1819-1849.

Monograph

Complete list of publications: cv

My profile on Google Scholar and Researchgate

Professional Services

  • Associate Editor for Annals of Applied Statistics.
  • Associate Editor for Biometrics (2010-2018).
  • Editorial Committee for Annual Review of Statistics and Its Application.
  • Associate Editor for Lifetime Data Analysis.
  • Statistics Editor for BiOMARKERS.
  • Statistical Advisor for The British Journal of Psychiatry (BJPsych Open).
  • International Biometric Society (IBS) Budget and Finance Committee: 2016-2023.
  • International Chinese Statistical Association (ICSA), Board of Director, 2024-2026.

Open Source

  • R code to evaluate HUM for three and four unordered categories, following Li, Fine and Pencina (2018) Statistical Theory and Related Fields. These files are now incorporated in an R package mcca available on CRAN and GitHub. May evaluate HUM for combined markers based on all sorts of learning methods. The package from GitHub allows Deep Learning.
  • R package HUM is now available on CRAN. May evaluate HUM for markers to differentiate large number (M>3) of categories.
  • R code to evaluate multi-category NRI and IDI, following Li, Jiang and Fine (2013) Biostatistics. Also incorporated in an R package mcca available on CRAN and GitHub.
  • R code to evaluate Polytomous Discrimination Index (PDI) for three and four classes, following Li, Feng, Fine, Pencina and Van Calster (2018) SMMR. Also incorporated in an R package mcca available on CRAN and GitHub.
  • R code for improvement screening, based on Yue and Li (2018) International Journal of Biostatistics.
  • R code to compute bootstrap confidence intervals and p-values for NRI and IDI. See Shao et al. (2015) Biomarkers.
  • R code for two stage multiple Change Point detection (TSMCP): example and scad penalty function following Li and Jin (2018) AOS.
  • ROC Matlab code: nonparametric and semiparametric ROC surface estimation. See Li and Zhou (2009) JSPI.

Lectures

  • ST1131: INTRODUCTION TO STATISTICS (Spring 2012)
  • ST2334: Probability and Statistics (Fall 2021)
  • DSC2008: Business Analytics (Fall 2015, Spring 2017)
  • ST3131: REGRESSION ANALYSIS (Spring 2011)
  • DSA4211: High-dimensional Statistical Analysis (Fall 2016)
  • ST4241: DESIGN AND ANALYSIS OF CLINICAL TRIALS (Fall 2011, 2013, 2014)
  • ST4242: ANALYSIS OF LONGITUDINAL DATA (Spring 2007, 2008, 2009, 2010, 2015)
  • ST4253: Applied Time Series Analysis (Fall 2022)
  • ST5203: EXPERIMENTAL DESIGN (Fall 2009)
  • ST5206: GENERALIZED LINEAR MODEL (Fall 2010)
  • ST5207: Nonparametric Regression (Fall 2017)
  • ST5212: SURVIVAL ANALYSIS (Fall 2007)
  • ST5217: Statistical Methods for Genetic Analysis (Spring 2014)
  • ST5222: Advanced Topics in Applied Statistics (Spring 2024)
  • ST5223: Statistical Models (Spring 2016, 2018)
  • ST5227: Applied Data Mining (Spring 2019, 2020, 2021, 2022)
  • ST5318: STATISTICAL METHODS FOR HEALTH SCIENCES (Fall 2006)