Researcher Profile
Researcher Profile
Runze Li, PhD
Verne M. Willaman Professor, Statistics
Professor, Department of Public Health Sciences
Director, Center for Statistical Genetics
Professor, Department of Public Health Sciences
Director, Center for Statistical Genetics
Scientific Program:Cancer Control
Recent Publications
2021
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
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 2021, 'Stable correlation and robust feature screening', Science China Mathematics. https://doi.org/10.1007/s11425-019-1702-5
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
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 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
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