Jialiang Li

Associate Professor
Department of Statistics & Applied Probability
National University of Singapore

             Phone: (65)6516-8932

Adjunct Appointment

Duke University-NUS Graduate Medical School
Singapore Eye Research Institute

Current Interests

Personalized medicine; Diagnostic medicine; Prediction; Smoothing; Statistical learning; Survival analysis.

Honours and Awards

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

Recent Works

  • 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. Accepted.
  • Li, J., Lv, J., Wan, A.K.T., Liao, J. (2020). AdaBoost semiparametric model averaging prediction for multiple categories. Journal of the American Statistical Association (T&M). Accepted.
  • Jiang, B., Song, R., Li, J., Zeng, D. (2019). Entropy learning for dynamic treatment regimes (with discussion). Statistica Sinica. 29:1633-1710.
  • 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. Applied Statistics (JRSSC). 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.


Complete list of publications: cv

My profile on Google Scholar and Researchgate

Professional Services

  • Associate Editor for Biometrics (2010-2018).
  • Associate Editor for Lifetime Data Analysis (since 2014).
  • Associate Editor for Biostatistics & Epidemiology (since 2016).
  • Associate Editor for Communications for Statistical Applications and Methods (since 2017).
  • Statistics Editor for BiOMARKERS (since 2010).

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.


  • DSC2008: Business Analytics (Fall 2015, Spring 2017)
  • ST3131: REGRESSION ANALYSIS (Spring 2011)
  • ST4241: DESIGN AND ANALYSIS OF CLINICAL TRIALS (Fall 2011, 2013, 2014)
  • ST4242: ANALYSIS OF LONGITUDINAL DATA (Spring 2007, 2008, 2009, 2010, 2015)
  • DSA4211: High-dimensional Statistical Analysis (Fall 2016)
  • ST5203: EXPERIMENTAL DESIGN (Fall 2009)
  • ST5207: Nonparametric Regression (Fall 2017)
  • ST5212: SURVIVAL ANALYSIS (Fall 2007)
  • ST5217: Statistical Methods for Genetic Analysis (Spring 2014)
  • ST5223: Statistical Models (Spring 2016, 2018)
  • ST5227: Applied Data Mining (Spring 2019, 2020)