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
  • Sample Size
  • Substance-Related Disorders
  • Genes
  • Parkinson Disease
  • Research Personnel
  • Smoking Cessation
  • Quantitative Trait Loci
  • Ecological Momentary Assessment
  • Datasets
  • Data Analysis
  • Linear Models

Recent Publications

2022

Na, M, Dou, N, Liao, Y, Rincon, SJ, Francis, LA, Graham-Engeland, JE, Murray-Kolb, LE & Li, R 2022, 'Daily Food Insecurity Predicts Lower Positive and Higher Negative Affect: An Ecological Momentary Assessment Study', Frontiers in Nutrition, vol. 9, 790519. https://doi.org/10.3389/fnut.2022.790519
Jimenez Rincon, S, Dou, N, Murray-Kolb, LE, Hudy, K, Mitchell, DC, Li, R & Na, M 2022, 'Daily food insecurity is associated with diet quality, but not energy intake, in winter and during COVID-19, among low-income adults', Nutrition Journal, vol. 21, no. 1, 19. https://doi.org/10.1186/s12937-022-00768-y
Cai, X, Coffman, DL, Piper, ME & Li, R 2022, 'Estimation and inference for the mediation effect in a time-varying mediation model', BMC Medical Research Methodology, vol. 22, no. 1, 113. https://doi.org/10.1186/s12874-022-01585-x
Zeng, M, Liao, Y, Li, R & Sudjianto, A 2022, 'Local Linear Approximation Algorithm for Neural Network', Mathematics, vol. 10, no. 3, 494. https://doi.org/10.3390/math10030494
Chen, H, Zou, CL & Li, RZ 2022, 'Projection-based High-dimensional Sign Test', Acta Mathematica Sinica, English Series, vol. 38, no. 4, pp. 683-708. https://doi.org/10.1007/s10114-022-0435-9
Guo, X, Li, R, Liu, J & Zeng, M 2022, 'Statistical inference for linear mediation models with high-dimensional mediators and application to studying stock reaction to COVID-19 pandemic', Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2022.03.001
Brown, G, Du, G, Farace, E, Lewis, MM, Eslinger, PJ, McInerney, J, Kong, L, Li, R, Huang, X & De Jesus, S 2022, 'Subcortical Iron Accumulation Pattern May Predict Neuropsychological Outcomes after Subthalamic Nucleus Deep Brain Stimulation: A Pilot Study', Journal of Parkinson's Disease, vol. 12, no. 3, pp. 851-863. https://doi.org/10.3233/JPD-212833
Li, R, Xu, K, Zhou, Y & Zhu, L 2022, 'Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative Covariances', Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2022.2044334
Guo, X, Ren, H, Zou, C & Li, R 2022, 'Threshold Selection in Feature Screening for Error Rate Control', Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2021.2011735

2021

Huang, Y, Li, C, Li, R & Yang, S 2022, 'An overview of tests on high-dimensional means', Journal of Multivariate Analysis, vol. 188, 104813. https://doi.org/10.1016/j.jmva.2021.104813
Li, Z, Wang, Q & Li, R 2021, 'Central limit theorem for linear spectral statistics of large dimensional Kendall's rank correlation matrices and its applications', Annals of Statistics, vol. 49, no. 3, pp. 1569-1593. https://doi.org/10.1214/20-AOS2013
Nandy, D, Chiaromonte, F & Li, R 2021, 'Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems', Journal of the American Statistical Association. https://doi.org/10.1080/01621459.2020.1864380
Huang, D, Zhu, X, Li, R & Wang, H 2021, 'Feature screening for network autoregression model', Statistica Sinica, vol. 31, no. 3, pp. 1239-1259. https://doi.org/10.5705/ss.202018-0400
Li, C, Wang, X, Du, G, Chen, H, Brown, G, Lewis, MM, Yao, T, Li, R & Huang, X 2021, 'Folded concave penalized learning of high-dimensional MRI data in Parkinson's disease', Journal of Neuroscience Methods, vol. 357, 109157. https://doi.org/10.1016/j.jneumeth.2021.109157
Xiao, D, Ke, Y & Li, R 2021, 'Homogeneity structure learning in large-scale panel data with heavy-tailed errors', Journal of Machine Learning Research, vol. 22.
Li, M, Li, R & Ma, Y 2021, 'Inference in high dimensional linear measurement error models', Journal of Multivariate Analysis, vol. 184, 104759. https://doi.org/10.1016/j.jmva.2021.104759
Zou, T, Lan, W, Li, R & Tsai, CL 2021, 'Inference on covariance-mean regression', Journal of Econometrics. https://doi.org/10.1016/j.jeconom.2021.05.004
Guo, X, Li, R, Liu, W & Zhu, L 2022, 'Stable correlation and robust feature screening', Science China Mathematics, vol. 65, no. 1, pp. 153-168. https://doi.org/10.1007/s11425-019-1702-5
Parikh, RB, Liu, M, Li, E, Li, R & Chen, J 2021, 'Trajectories of mortality risk among patients with cancer and associated end-of-life utilization', npj Digital Medicine, vol. 4, no. 1, 104. https://doi.org/10.1038/s41746-021-00477-6
Buu, A, Cai, Z, Li, R, Wong, SW, Lin, HC, Su, WC, Jorenby, DE & Piper, ME 2021, 'Validating E-Cigarette Dependence Scales Based on Dynamic Patterns of Vaping Behaviors', Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco, vol. 23, no. 9, pp. 1484-1489. https://doi.org/10.1093/ntr/ntab050
Wang, J, Cai, X & Li, R 2021, 'Variable selection for partially linear models via Bayesian subset modeling with diffusing prior', Journal of Multivariate Analysis, vol. 183, 104733. https://doi.org/10.1016/j.jmva.2021.104733
Liao, Y, Liu, J, Coffman, DL & Li, R 2021, 'Varying Coefficient Mediation Model and Application to Analysis of Behavioral Economics Data', Journal of Business and Economic Statistics. https://doi.org/10.1080/07350015.2021.1971089

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 2022, 'Large-Scale Datastreams Surveillance via Pattern-Oriented-Sampling', Journal of the American Statistical Association, vol. 117, no. 538, pp. 794-808. https://doi.org/10.1080/01621459.2020.1819295
Liu, W, Ke, Y, Liu, J & Li, R 2022, 'Model-Free Feature Screening and FDR Control With Knockoff Features', Journal of the American Statistical Association, vol. 117, no. 537, pp. 428-443. 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 2021, 'Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation', Journal of the American Statistical Association, vol. 116, no. 535, pp. 1307-1318. 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-AOS1779
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

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