Publications

Preprints

2021

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2020

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2019

  • Yang, S., Kim, J. and Song, R. (2019) “Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data“. Accepted at Journal of the Royal Statistical Society, Series B.
  • Yu, M., Lu, W. and Song, R. (2019) “A New Framework for Online Testing of Heterogeneous Treatment Effect”. Accepted at the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20).
  • Pan, M, Li, Y., Zhou, X., Liu, Z., Song, R. , Liu, H. and Luo, J. (2019) “DHPA: Dynamic Human Preference Analytics Framework— A Case Study on Taxi Drivers’ Learning Curve Analysis”. Accepted at the ACM Transactions on Intelligent Systems and Technology.
  • Shi, C., Song, R. and Lu, W. (2019) “Concordance and Value Information Criteria for Optimal Treatment Decision”. Accepted at the Annals of Statistics. [paper] and [supplementary file]
  • Su, L,, Lu, W. Song, R. and Huang, D. (2019) “Testing and Estimation of Social Network Dependence with Time to Event Data”. Accepted at Journal of American Statistical Association. [paper]
  • Shi, C., Lu, W. and Song, R.  (2019) “A Sparse Random Projection-based Test for Overall Qualitative Treatment Effects”. Accepted at Journal of American Statistical Association. [paper] and [supplementary file]
  • Su, L,, Lu, W., and Song, R.  (2019) “Modeling and Estimation for Optimal Treatment Decision with Interference”. Accepted at STAT. [paper]
  • Pan, M, Li, Y., Zhou, X., Liu, Z., Song, R. , Liu, H. and Luo, J. (2019) “Dissecting the Learning Curve of of Taxi Drivers: A Data-Driven Approach”. Accepted at the SIAM International Conference on Data Mining (SDM19). (Acceptance rate 22.7%). Won the award for Best Applied Data Science Paper. [paper]

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2018

  • Shi, C., Lu, W. and Song, R. (2018+) “Determining the Number of Latent Factors in Statistical Multi-Relational Learning”. To appear in Journal of Machine Learning Research. [paper]
  • Jiang, B., Song, R., Zeng, D. and Li, J. (2018+) “Entropy Learning for Dynamic Treatment Regimes”, with discussions. To appear in Statistica Sinica. [paper] and [rejoinder]
  • Shi, C., Song, R., Chen, Z. and Li, R. (2018+) “Linear hypothesis testing for high dimensional generalized linear models”. To appear in the Annals of Statistics. [paper] and [supplementary file]
  • Liang, S., Lu, W. and Song, R., (2018+) “Deep advantage learning for optimal dynamic treatment regime.” To appear in Statistics and Related Fields. [link]
  • Shi, C., Lu, W. and Song, R., (2018+) “On Testing Conditional Qualitative Treatment Effects ”. To appear in the Annals of Statistics. [link]
  • Zhu, W., Zeng, D. and Song, R., (2018+) “Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes.” To appear in Journal of American Statistical Association. [link]
  • Shi, C., Song, R., Lu, W., (2018+) “Discussion of ’Optimal treatment allocations in space and time for on-line control of an emerging infectious disease’ ”. To appear in Journal of the Royal Statistical Society, Series C.
  • Shi, C., Song, R., Lu, W., and Fu, B., (2018+) “Maximin-Projection Learning for Optimal Treatment Decision with Heterogeneous Data.” To appear in Journal of the Royal Statistical Society, Series B. [link]
  • Liang, S., Lu, W., Song, R., and Wang, L. (2018+). “Sparse concordance-assisted learning for optimal treatment decision.” To appear in Journal of Machine Learning Research. [link]
  • Luo, S., Song, R., Styner, M., Gilmore, J. and Zhu, H., (2018) “FSEM: Functional Structural Equation Models for Twin Functional Data.” Journal of American Statistical Association, DOI: 10.1080/01621459.2017.1407773. [link]

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2017

  • Shi, C., Fan, A, Song, R. and Lu, W. (2017) “High-dimensional A-learning for Dynamic Treatment Regimes.” To appear in the Annals of Statistics. [link]
  • Shi, C., Song, R., Lu, W., (2017) “Discussion of ’Random Projection Ensemble Classification’. Journal of the Royal Statistical Society, Series B, 79, 959-1035.
  • Kang, S., Lu, W. and Song, R., (2017) “Subgroup Detection and Sample Size Calculation with Proportional Hazards Regression for Survival Data”, Statistics in Medicine. DOI: 10.1002/sim.7441 [link]
  • Jiang, R., Lu, W., Song, R., Hudgens, M.G. and Naprvavnik, S, (2017) “Doubly Robust Estimation of Optimal Treatment Regimes for Survival Data.” the Annals of Applied Statistics, 11, 1763– 1786. [link]
  • Shi, C., Lu, W. and Song, R., (2017) “A massive data framework for M-estimators with cubic-rate.” Journal of American Statistical Association, DOI: 10.1080/01621459.2017.1360779. [link]
  • Wang, L., Zhou, Y. Song, R., and Sherwood, (2017) “Quantile-Optimal Treatment Regimens.” Journal of American Statistical Association, DOI: 10.1080/01621459.2017.1330204. [link]
  • Lu, Z, Song, R., Zeng, D and Zhang, J., (2017)“Principal Component Adjusted Screening for High-dimensional Data.” Computational Statistics and Data Analysis. 110:134-144. [link]
  • Song, R., Luo, S., Zeng, D., Zhang, H. H., Lu, W. and Li, Z, (2017)“Semiparametric single-index model for estimating optimal individualized treatment strategy.” Electronic Journal of Statistics, 11(1) 364-384. [link]

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2016

  • Fan, C., Lu, W., Song, R. and Zhou, Y, (2016) “Concordance-assisted learning for estimating optimal individualized treatment regimes.” Journal of the Royal Statistical Society, Series B : doi:10.1111/rssb.12216 [link]
  • Bai, X, Tsiatis, Lu, W and Song, R., (2016) “Optimal treatment regimes for survival endpoints using locally-efficient doubly-robust estimator from a classification perspective.” Lifetime Data Analysis. 1–20. [link]
  • Shi, C. Song, R. and Lu, W. (2016) “Robust Learning for Optimal Treatment Regimes with NPDimensionality.” Electronic Journal of Statistics. 10(2), 2894–2921. [link]
  • Jiang, R, Lu, W, Song, R., and Davidian, M., (2016) “On estimation of optimal treatment regimes for maximizing t-year survival probability.” Journal of the Royal Statistical Society, Series B. DOI: 10.1111/rssb.12201 [link]
  • Chen, J., Liu Y., Zeng, D., Song, R., Zhao, Y. and Kosorok, M.R., (2016) “Comment on ‘Bayesian Nonparametric Estimation for Dynamic Treatment Regimes with Sequential Transition Times.’ ” To appear at Journal of American Statistical Association.
  • Fan, A., Song, R. and Lu, W., (2016) “Change-Plane Analysis for Subgroup Detection and Sample Size Calculation”. Journal of American Statistical Association. DOI:10.1080/01621459.2016.1166115 [link]
  • Laber, EB, Zhao, Y., Regh, T., Davidian, M., Tsiatis, A., Stanford, J.B., Zeng, D., Song, R. and Kosorok MR., (2016) “Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy.” Statistics in Medicine. DOI: 10.1002/sim.6783 [link]
  • Song, R., Banerjee, M, and Kosorok, MR, (2016) “Asymptotics for change-point models under varying degrees of mis-specification.” the Annals of Statistics. 44(1), 153-182 [link]
  • Fan, A, Lu, W. Song, R., (2016) “Sequential Advantage Selection for Optimal Treatment Regimens”. the Annals of Applied Statistics. 10(1), 32-53. [link]

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2015

  • Song, R., Zeng, D, Laber, E, Zhao, Y, Yuan M, and Kosorok, MR, (2015) “On Sparse Representation for Outcome-Weighted Learning.” STAT DOI: 10.1002/sta4.78. [link]
  • Bradic, J and Song, R., (2015) “Gaussian Oracle Inequalities for Structured Selection in NonParametric Cox Model.” Electronic Journal of Statistics. 9(1) 492-534. [link]

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2014

  • Song, R., Wang, W, Zeng, D and Kosorok, MR, (2014) “Penalized Q-learning for Dynamic Treatment Regimes.” Statistical Sinica. 25(3):901-920. [link]
  • Zhao, Y, Zeng, D, Laber, E, Song, R., Yuan M, and Kosorok, MR, (2014) “Doubly Robust Learning for Estimating Individualized Treatment with Censored Data.” Biometrika. doi: 10.1093/biomet/asu050 [link]
  • Goldberg, Y, Song, R., Zeng, D. and Kosorok, MR (2014), “Comment on ‘Dynamic treatment regimes: technical challenges and applications’.” Electronic Journal of Statistics.
  • Song, R., Lu, W, Ma, S and Jeng, J, (2014) “Censored Rank Independence Screening for High-dimensional Survival Data.” Biometrika. doi: 10.1093/biomet/asu047 [link]
  • Song, R., Kosorok, M.R. and Fine, J.P. (2014) Comment on ”Multiscale change point inference” by Frick, Munk and Sieling. Journal of the Royal Statistical Society, Series B. 76(3), 564.
  • Song, R., Yi, F and Zou, H, (2014) “On Varying-coefficient Independence Screening for High-dimensional data.” Statistical Sinica. 24(4), 1735-1752. [link]

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2012

  • Goldberg, Y, Song, R. and Kosorok, MR (2012), “Adaptive Q-learning,” IMS Collections: From Probability to Statistics and Back: High-Dimensional Models and Processes 9: 150-162. [link]
  • Song, R., Huang, J and Ma, S (2012), “Integrative Prescreening in Analysis of Multiple Cancer Genomic Studies,” BMC Bioinformatics 13:168. [link]

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2011

  • Fan, J., Feng, Y. and Song, R. (2011), “Nonparametric Independence Screening in Sparse Ultra-High Dimensional Additive Models,” Journal of American Statistical Association, 106, 544-557. [link]
  • Zhou, H., Song, R., and Qin, J. (2011), “Statistical inference for a two-stage outcome-dependent sampling design with a continuous outcome,” Biometrics, 67, 194-202. [link]

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2010

  • Fan, J and Song, R. (2010) “Sure independence screening in generalized linear models with npdimensionality,” the Annals of Statistics, 38(6), 3567-3604. [link]
  • Song, R. and Cai, J. (2010). “Joint covariate-adjusted score test statistics for recurrent events and a terminal event,” In Special issue: Recurrent Events of Lifetime Data Analysis, 16(4), 491-508. [link]

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2009

  • Anand, I., Carson, P., Galle E., Song, R. , Boehmer, J., Ghali, K.J., Jaski, B., Lindenfeld, J., O’Connor, C., Steinberg, R.J., Leigh, J., Yong, P., Kosorok, M. R., Feldman, A.M., DeMets, D. and Bristow, M. R. (2009). “Cardiac Resynchronization Therapy Reduces the Risk of Hospitalizations in Patients With Advanced Heart Failure: Results From the Comparison of Medical Therapy, Pacing and Defibrillation in Heart Failure (COMPANION) Trial,” Circulation, 119: 969-977. [link]
  • Song, R., Zhou, H. and Kosorok, M. R. (2009). “On Semiparametric efficient inference for Two-stage outcome-dependent-sampling with a continuous outcome,” Biometrika, 96, 221-228. [link]
  • Song, R., Kosorok, M. R. and Fine, J. P. (2009). “On Asymptotically optimal tests under loss of identifiability in semiparametric models,” Annals of Statistics, 37, 2409-2444. [link]

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2008

  • Song, R., Kosorok, M. R. and Cai, J. (2008). “Robust covariate-adjusted log-rank statistics and corresponding sample size formula for recurrent events data,” Biometrics, 64, 741-750. [link]
  • Meunier, J., Song, R., Scott, R. L., Doherty, E. K., David O. E., Andersen, E. and Bruggink, G. J. (2008). “Proximate cues for a short distance migratory species: A new application of survival analysis,” Journal of Wildlife Management, 72(2): 440–448. [link]

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2007

  • Song, R., Cook, T. D., Kosorok, M. R. (2007). “What we want Versus what we can get: A closer look at endpoints for cardiovascular studies,” Journal of Biopharmaceutical Statistics, 18(2), 370-381.
  • Kosorok, M. R. and Song, R. (2007). “Inference under right censoring for transformation models with a change-point based on a covariate threshold,” Annals of Statistics, 35, 957-989. [link]