Researcher Profile

Researcher Profile

Runze Li, PhD

Runze Li, PhD

Verne M. Willaman Professor, Statistics
Professor, Department of Public Health Sciences
Director, Center for Statistical Genetics
Scientific Program:Cancer Control

Research Interests

  • Smoking
  • Substance-Related Disorders
  • Sample Size
  • Genes
  • Research Personnel
  • Quantitative Trait Loci
  • Smoking Cessation
  • Datasets
  • Linear Models
  • Parkinson Disease
  • Tobacco Use Disorder
  • Marijuana Use

Recent Publications


Zou, C, Wang, G & Li, R 2020, 'Consistent selection of the number of change-points via sample-splitting', Annals of Statistics, vol. 48, no. 1, pp. 413-439.
Buu, A, Yang, S, Li, R, Zimmerman, MA, Cunningham, RM & Walton, MA 2020, 'Examining measurement reactivity in daily diary data on substance use: Results from a randomized experiment', Addictive Behaviors, vol. 102, 106198.
Chu, W, Li, R, Liu, J & Reimherr, M 2020, 'Feature selection for generalized varying coefficient mixed-effect models with application to obesity gwas', Annals of Applied Statistics, vol. 14, no. 1, pp. 276-298.
Dziak, JJ, Coffman, DL, Lanza, ST, Li, R & Jermiin, LS 2020, 'Sensitivity and specificity of information criteria', Briefings in bioinformatics, vol. 21, no. 2, pp. 553-565.
Shi, C, Song, R, Lu, W & Li, R 2020, 'Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation', Journal of the American Statistical Association.
Liu, W & Li, R 2020, Variable Selection and Feature Screening. in Advanced Studies in Theoretical and Applied Econometrics. Advanced Studies in Theoretical and Applied Econometrics, vol. 52, Springer, pp. 293-326.


Wang, L, Ma, J, Dholakia, R, Howells, C, Lu, Y, Chen, C, Li, R, Murray, M & Leslie, D 2019, 'Changes in healthcare expenditures after the autism insurance mandate', Research in Autism Spectrum Disorders, vol. 57, pp. 97-104.
Yang, G, Zhang, L, Li, R & Huang, Y 2019, 'Feature screening in ultrahigh-dimensional varying-coefficient Cox model', Journal of Multivariate Analysis, vol. 171, pp. 284-297.
Zhong, PS, Li, R & Santo, S 2019, 'Homogeneity tests of covariance matrices with high-dimensional longitudinal data', Biometrika, vol. 106, no. 3, pp. 619-634.
Zheng, S, Chen, Z, Cui, H & Li, R 2019, 'Hypothesis testing on linear structures of high-dimensional covariance matrix', Annals of Statistics, vol. 47, no. 6, pp. 3300-3334.
Shi, C, Song, R, Chen, Z & Li, R 2019, 'Linear hypothesis testing for high dimensional generalized linear models', Annals of Statistics, vol. 47, no. 5, pp. 2671-2703.
Zhu, X, Chang, X, Li, R & Wang, H 2019, 'Portal nodes screening for large scale social networks', Journal of Econometrics, vol. 209, no. 2, pp. 145-157.
Dziak, JJ, Coffman, DL, Reimherr, M, Petrovich, J, Li, R, Shiffman, S & Shiyko, MP 2019, 'Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists', Statistics Surveys, vol. 13, pp. 150-180.
Li, M, Ma, Y & Li, R 2019, 'Semiparametric regression for measurement error model with heteroscedastic error', Journal of Multivariate Analysis, vol. 171, pp. 320-338.
Liu, W, Li, R, Zimmerman, MA, Walton, MA, Cunningham, RM & Buu, A 2019, 'Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use', Addictive Behaviors, vol. 94, pp. 147-155.
Trucco, EM, Yang, S, Yang, JJ, Zucker, RA, Li, R & Buu, A 2020, 'Time-varying Effects of GABRG1 and Maladaptive Peer Behavior on Externalizing Behavior from Childhood to Adulthood: Testing Gene × Environment × Development Effects', Journal of youth and adolescence, vol. 49, no. 7, pp. 1351-1364.
Wang, L, Chen, Z, Wang, CD & Li, R 2020, 'Ultrahigh dimensional precision matrix estimation via refitted cross validation', Journal of Econometrics, vol. 215, no. 1, pp. 118-130.


Li, R, Ren, JJ, Yang, G & Yu, Y 2018, 'Asymptotic behavior of Cox's partial likelihood and its application to variable selection', Statistica Sinica, vol. 28, no. 4, pp. 2713-2731.
Dierker, L, Selya, A, Lanza, S, Li, R & Rose, J 2018, 'Depression and marijuana use disorder symptoms among current marijuana users', Addictive Behaviors, vol. 76, pp. 161-168.
Buu, A & Li, R 2018, New statistical methods inspired by data collected from alcohol and substance abuse research. in Alcohol Use Disorders: A Developmental Science Approach to Etiology. Oxford University Press, pp. 354-366.
Liu, H, Wang, X, Yao, T, Li, R & Ye, Y 2019, 'Sample average approximation with sparsity-inducing penalty for high-dimensional stochastic programming', Mathematical Programming, vol. 178, no. 1-2, pp. 69-108.
Kürüm, E, Hughes, J, Li, R & Shiffman, S 2018, 'Time-varying copula models for longitudinal data', Statistics and its Interface, vol. 11, no. 2, pp. 203-221.
Liu, J, Lou, L & Li, R 2018, 'Variable selection for partially linear models via partial correlation', Journal of Multivariate Analysis, vol. 167, pp. 418-434.


Yang, S, Cranford, JA, Jester, JM, Li, R, Zucker, RA & Buu, A 2017, 'A time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes with applications in substance abuse research', Statistics in Medicine, vol. 36, no. 5, pp. 827-837.
Yang, S, Cranford, JA, Li, R, Zucker, RA & Buu, A 2017, 'A time-varying effect model for studying gender differences in health behavior', Statistical Methods in Medical Research, vol. 26, no. 6, pp. 2812-2820.
Zhang, L, Wang, X, Wang, M, Sterling, NW, Du, G, Lewis, MM, Yao, T, Mailman, RB, Li, R & Huang, X 2017, 'Circulating cholesterol levels may link to the factors influencing Parkinson's Risk', Frontiers in Neurology, vol. 8, no. SEP, 501.
Du, G, Lewis, MM, Kanekar, S, Sterling, NW, He, L, Kong, L, Li, R & Huang, X 2017, 'Combined diffusion tensor imaging and apparent transverse relaxation rate differentiate Parkinson disease and atypical parkinsonism', American Journal of Neuroradiology, vol. 38, no. 5, pp. 966-972.
Chen, Z, Fan, J & Li, R 2018, 'Error Variance Estimation in Ultrahigh-Dimensional Additive Models', Journal of the American Statistical Association, vol. 113, no. 521, pp. 315-327.
Liu, H, Yao, T, Li, R & Ye, Y 2017, 'Folded concave penalized sparse linear regression: sparsity, statistical performance, and algorithmic theory for local solutions', Mathematical Programming, vol. 166, no. 1-2, pp. 207-240.
Zhu, L, Xu, K, Li, R & Zhong, W 2017, 'Projection correlation between two random vectors', Biometrika, vol. 104, no. 4, pp. 829-843.
Miao, J, Chen, Z, Wang, Z, Shrestha, S, Li, X, Li, R & Cui, L 2017, 'Sex-specific biology of the human malaria parasite revealed from the proteomes of mature male and female gametocytes', Molecular and Cellular Proteomics, vol. 16, no. 4, pp. 537-551.
Percival, CJ, Kawasaki, K, Huang, Y, Weiss, K, Jabs, EW, Li, R & Richtsmeier, JT 2017, The contribution of angiogenesis to variation in bone development and evolution. in Building Bones: Bone Formation and Development in Anthropology. Cambridge University Press, pp. 26-51.
Ma, S, Li, R & Tsai, CL 2017, 'Variable Screening via Quantile Partial Correlation', Journal of the American Statistical Association, vol. 112, no. 518, pp. 650-663.
Li, R, Liu, J & Lou, L 2017, 'Variable selection via partial correlation', Statistica Sinica, vol. 27, no. 3, pp. 983-996.


Wang, N, Gosik, K, Li, R, Lindsay, B & Wu, R 2016, 'A block mixture model to map eQTLs for gene clustering and networking', Scientific reports, vol. 6, 21193.
Zhang, X, Wu, Y, Wang, L & Li, R 2016, 'A consistent information criterion for support vector machines in diverging model spaces', Journal of Machine Learning Research, vol. 17.
Kürüm, E, Hughes, J & Li, R 2016, 'A semivarying joint model for longitudinal binary and continuous outcomes', Canadian Journal of Statistics, vol. 44, no. 1, pp. 44-57.
Li, R 2016, 'Editorial', Annals of Statistics, vol. 44, no. 5, pp. 1817-1820.
Chu, W, Li, R & Reimherr, M 2016, 'Feature screening for time-varying coefficient models with ultrahigh-dimensional longitudinal data', Annals of Applied Statistics, vol. 10, no. 2, pp. 596-617.
Yang, G, Yu, Y, Li, R & Buu, A 2016, 'Feature screening in ultrahigh dimensional Cox's model', Statistica Sinica, vol. 26, no. 3, pp. 881-901.
Liu, H, Du, G, Zhang, L, Lewis, M, Wang, X, Yao, T, Li, R & Huang, X 2016, 'Folded concave penalized learning in identifying multimodal MRI marker for Parkinson's disease', Journal of Neuroscience Methods, vol. 268, pp. 1-6.
Liu, H, Yao, T & Li, R 2016, 'Global solutions to folded concave penalized nonconvex learning', Annals of Statistics, vol. 44, no. 2, pp. 629-659.
Li, D & Li, R 2016, 'Local composite quantile regression smoothing for Harris recurrent Markov processes', Journal of Econometrics, vol. 194, no. 1, pp. 44-56.
Wang, LH, Liu, JY, Li, Y & Li, RZ 2017, 'Model-free conditional independence feature screening for ultrahigh dimensional data', Science China Mathematics, vol. 60, no. 3, pp. 551-568.
Xu, C, Zhang, Y, Li, R & Wu, X 2016, 'On the Feasibility of Distributed Kernel Regression for Big Data', IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 11, 7520638, pp. 3041-3052.
Liu, X, Cui, Y & Li, R 2016, 'Partial linear varying multi-index coefficient model for integrative gene-environment interactions', Statistica Sinica, vol. 26, no. 3, pp. 1037-1060.
Xu, C, Lin, S, Fang, J & Li, R 2016, 'Prediction-based termination rule for greedy learning with massive data', Statistica Sinica, vol. 26, no. 2, pp. 841-860.
Zhong, W, Zhu, L, Li, R & Cui, H 2016, 'Regularized quantile regression and robust feature screening for single index models', Statistica Sinica, vol. 26, no. 1, pp. 69-95.
Lan, W, Zhong, PS, Li, R, Wang, H & Tsai, CL 2016, 'Testing a single regression coefficient in high dimensional linear models', Journal of Econometrics, vol. 195, no. 1, pp. 154-168.
Kürüm, E, Li, R, Shiffman, S & Yao, W 2016, 'Time-varying coefficient models for joint modeling binary and continuous outcomes in longitudinal data', Statistica Sinica, vol. 26, no. 3, pp. 979-1000.
Yang, H, Li, R, Zucker, RA & Buu, A 2016, 'Two-stage model for time varying effects of zero-inflated count longitudinal covariates with applications in health behaviour research', Journal of the Royal Statistical Society. Series C: Applied Statistics, vol. 65, no. 3, pp. 431-444.
Pan, R, Wang, H & Li, R 2016, 'Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening', Journal of the American Statistical Association, vol. 111, no. 513, pp. 169-179.

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