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
ril4@psu.edu

Research Interests

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

Recent Publications

2020

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. https://doi.org/10.1016/j.addbeh.2019.106198
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. https://doi.org/10.1080/01621459.2019.1710154
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. https://doi.org/10.1007/978-3-030-31150-6_10

2019

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. https://doi.org/10.1016/j.rasd.2018.10.004
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. https://doi.org/10.1016/j.jmva.2018.12.009
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. https://doi.org/10.1214/18-AOS1781
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. https://doi.org/10.1214/18-AOS1761
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. https://doi.org/10.1016/j.jeconom.2018.12.021
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. https://doi.org/10.1214/19-SS126
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. https://doi.org/10.1016/j.jmva.2018.12.012
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. https://doi.org/10.1016/j.addbeh.2018.12.024
Trucco, EM, Yang, S, Yang, JJ, Zucker, RA, Li, R & Buu, A 2019, '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. https://doi.org/10.1007/s10964-019-01171-3
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. https://doi.org/10.1016/j.jeconom.2019.08.004

2018

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. https://doi.org/10.5705/ss.202016.0401
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. https://doi.org/10.1016/j.addbeh.2017.08.013
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. https://doi.org/10.1093/oso/9780190676001.003.0021
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. https://doi.org/10.1007/s10107-018-1278-0
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. https://doi.org/10.4310/SII.2018.v11.n2.a1
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. https://doi.org/10.1016/j.jmva.2018.06.005

2017

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. https://doi.org/10.1002/sim.7177
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. https://doi.org/10.1177/0962280215610608
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. https://doi.org/10.3389/fneur.2017.00501
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. https://doi.org/10.3174/ajnr.A5136
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. https://doi.org/10.1080/01621459.2016.1251440
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. https://doi.org/10.1007/s10107-017-1114-y
Zhu, L, Xu, K, Li, R & Zhong, W 2017, 'Projection correlation between two random vectors', Biometrika, vol. 104, no. 4, pp. 829-843. https://doi.org/10.1093/biomet/asx043
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. https://doi.org/10.1074/mcp.M116.061804
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. https://doi.org/10.1017/9781316388907.003
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. https://doi.org/10.1080/01621459.2016.1156545
Li, R, Liu, J & Lou, L 2017, 'Variable selection via partial correlation', Statistica Sinica, vol. 27, no. 3, pp. 983-996. https://doi.org/10.5705/ss.202015.0473

2016

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. https://doi.org/10.1038/srep21193
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. https://doi.org/10.1002/cjs.11273
Li, R 2016, 'Editorial', Annals of Statistics, vol. 44, no. 5, pp. 1817-1820. https://doi.org/10.1214/16-AOS1494
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. https://doi.org/10.1214/16-AOAS912
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. https://doi.org/10.5705/ss.2014.171
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. https://doi.org/10.1016/j.jneumeth.2016.04.016
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. https://doi.org/10.1214/15-AOS1380
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. https://doi.org/10.1016/j.jeconom.2016.04.002
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. https://doi.org/10.1007/s11425-016-0186-8
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. https://doi.org/10.1109/TKDE.2016.2594060
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. https://doi.org/10.5705/ss.202015.0114
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. https://doi.org/10.5705/ss.202014.0068
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. https://doi.org/10.5705/ss.2014.049
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. https://doi.org/10.1016/j.jeconom.2016.05.016
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. https://doi.org/10.5705/ss.2014.213
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. https://doi.org/10.1111/rssc.12123
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. https://doi.org/10.1080/01621459.2014.998760

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