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

View Details
  • Smoking
  • Sample Size
  • Substance-Related Disorders
  • Genes
  • Research Personnel
  • Quantitative Trait Loci
  • Smoking Cessation
  • Data Analysis
  • Datasets
  • Genome-Wide Association Study
  • Linear Models
  • Parkinson Disease
View Details

Recent Publications

View Details

2020

Wang, L, Peng, B, Bradic, J, Li, R & Wu, Y 2020, 'A Tuning-free Robust and Efficient Approach to High-dimensional Regression', Journal of the American Statistical Association, vol. 115, no. 532, pp. 1700-1714. https://doi.org/10.1080/01621459.2020.1840989
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. <https://projecteuclid.org/euclid.aos/1581930141>
Li, X, Li, R, Xia, Z & Xu, C 2020, 'Distributed feature screening via componentwise debiasing', Journal of Machine Learning Research, vol. 21.
Cui, X, Li, R, Yang, G & Zhou, W 2020, 'Empirical likelihood test for a large-dimensional mean vector', Biometrika, vol. 107, no. 3, pp. 591-607. https://doi.org/10.1093/biomet/asaa005
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
Yang, G, Yang, S & Li, R 2020, 'Feature screening in ultrahigh-dimensional generalized varying-coefficient models', Statistica Sinica, vol. 30, no. 2, pp. 1049-1067. https://doi.org/10.5705/ss.202017.0362
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. https://doi.org/10.1214/19-AOAS1310
Ren, H, Zou, C, Chen, N & Li, R 2020, 'Large-Scale Datastreams Surveillance via Pattern-Oriented-Sampling', Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2020.1819295
Liu, W, Ke, Y, Liu, J & Li, R 2020, 'Model-Free Feature Screening and FDR Control With Knockoff Features', Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2020.1783274
Zhou, T, Zhu, L, Xu, C & Li, R 2020, 'Model-Free Forward Screening Via Cumulative Divergence', Journal of the American Statistical Association, vol. 115, no. 531, pp. 1393-1405. https://doi.org/10.1080/01621459.2019.1632078
Cai, Z, Li, R & Zhu, L 2020, 'Online sufficient dimension reduction through sliced inverse regression', Journal of Machine Learning Research, vol. 21.
Yang, S, Wen, J, Eckert, ST, Wang, Y, Liu, DJ, Wu, R, Li, R & Zhan, X 2020, 'Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning', Bioinformatics, vol. 36, no. 12, pp. 3811-3817. https://doi.org/10.1093/bioinformatics/btaa229
Wang, L, Peng, B, Bradic, J, Li, R & Wu, Y 2020, 'Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression”', Journal of the American Statistical Association, vol. 115, no. 532, pp. 1726-1729. https://doi.org/10.1080/01621459.2020.1843865
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. https://doi.org/10.1093/bib/bbz016
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
Fang, EX, Ning, Y & Li, R 2020, 'Test of significance for high-dimensional longitudinal data', Annals of Statistics, vol. 48, no. 5, pp. 2622-2645. https://doi.org/10.1214/19-AOS1900
Buu, A, Cai, Z, Li, R, Wong, SW, Lin, HC, Su, WC, Jorenby, DE & Piper, ME 2021, 'The association between short-term emotion dynamics and cigarette dependence: A comprehensive examination of dynamic measures', Drug and alcohol dependence, vol. 218, 108341. https://doi.org/10.1016/j.drugalcdep.2020.108341
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
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. https://doi.org/10.1093/biomet/asz011
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 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. 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
View Details

Clinical Trials Search