# 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 *

### Education

- PhD in Statistics 2006

*University of Wisconsin, Madison* - MS in Population Health Sciences 2005

*University of Wisconsin, Madison* - BS in Statistics 2001

*University of Science and Technology of China*

### Current Interests

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

### Recent Works

- Huang, L., Li, J. (2019). Weighted Volume Under the Three-way Receiver Operating Characteristic Surface.
*Statistical Methods in Medical Research*. Accepted. - Li, J., Gao, M., D’Agostino, R. (2019). Evaluating Classification Accuracy for Modern Learning Approaches.
*Statistics in Medicine (Tutorials in Biostatistics).*Accepted. - Wang, J. [PhD student], Li, J., Li, Y., Wong, W.K. (2019). A model-based multi-threshold method for subgroup identification.
*Statistics in Medicine.*Accepted. - Li, J and Wong, W.K. (2019). Comments on “Covariate-assisted ranking and screening for largescale two-sample inference” by Cai, Sun and Wang. JRSSB. 227.
- Li, J., Yue, M., Zhang, W. (2019). Subgroup Identification via Homogeneity Pursuit for Dense Longitudinal/Spatial Data.
*Statistics in Medicine.*Accepted. - Li, J., Xia, X., Wong, W.K., Nott, D. (2018). Varying coefficient semiparametric model average prediction.
*Biometrics.*74, 1417–1426. - Li, J., Feng, Q., Fine, J.P., Pencina, M.J., Van Calster, B. (2018). Nonparametric estimation and inference for polytomous discrimination index.
*Statistical Methods in Medical Research.*27(10): 3092—3103. - Li, J., Jin, B. (2018). Multi-threshold Accelerated Failure Time Model.
*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. - Fang, X. [PhD student], Li, J., Wong, W. K., and Fu, B. (2016). Detecting the Violation of Variance Homogeneity in Mixed Models: a Case Study.
*Statistical Methods in Medical Research*. 25(6): 2506-2520. - 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. - Natalia Novoselova, Cristina Della Beffa, Junxi Wang , Jialiang Li, Frank Pessler, Frank Klawonn (2014). HUM Calculator and HUM package for R: easy-to-use software tools for multicategory receiver operating characteristic analysis.
*Bioinformatics*. 30(11):1635-6. - 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.

#### Monograph

- Li, J. and Ma, S. (2013). Survival Analysis in Medicine and Genetics. Chapman & Hall/CRC Press.

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.*

### Lectures

- ST1131: INTRODUCTION TO STATISTICS (Spring 2012)
- 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)
- ST5206: GENERALIZED LINEAR MODEL (Fall 2010)
- ST5207: Nonparametric Regression (Fall 2017)
- ST5212: SURVIVAL ANALYSIS (Fall 2007)
- ST5217: Statistical Methods for Genetic Analysis (Spring 2014)
- ST5223: Statistical Models (Spring 2016)
- ST5318: STATISTICAL METHODS FOR HEALTH SCIENCES (Fall 2006)