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

Recent Publications

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
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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
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
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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
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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
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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
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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
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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
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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
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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
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2015

Wang, L, Peng, B & Li, R 2015, 'A High-Dimensional Nonparametric Multivariate Test for Mean Vector', Journal of the American Statistical Association, vol. 110, no. 512, pp. 1658-1669. https://doi.org/10.1080/01621459.2014.988215
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Fan, J & Li, R 2015, Local modeling: Density estimation and nonparametric regression. in Advanced Medical Statistics. World Scientific Publishing Co., pp. 1125-1171. https://doi.org/10.1142/9789814583312_0030
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Yang, H, Cranford, JA, Li, R & Buu, A 2015, 'Two-stage model for time-varying effects of discrete longitudinal covariates with applications in analysis of daily process data' Statistics in Medicine, vol. 34, no. 4, pp. 571-581. https://doi.org/10.1002/sim.6368
Yi, GY, Tan, X & Li, R 2015, 'Variable selection and inference procedures for marginal analysis of longitudinal data with missing observations and covariate measurement error', Canadian Journal of Statistics, vol. 43, no. 4, pp. 498-518. https://doi.org/10.1002/cjs.11268
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