Shu Yang

Liu, S., Yang, S., Zhang, Y., & Liu, G. (2024). Multiply robust estimators in longitudinal studies with missing data under control-based imputation. BIOMETRICS, 80(1). https://doi.org/10.1093/biomtc/ujad036

Lee, D., Gao, C., Ghosh, S., & Yang, S. (2024, March 24). Transporting survival of an HIV clinical trial to the external target populations. JOURNAL OF BIOPHARMACEUTICAL STATISTICS, Vol. 3. https://doi.org/10.1080/10543406.2024.2330216

Lee, D., Yang, S., Berry, M., Stinchcombe, T., Cohen, H. J., & Wang, X. (2024, April 8). genRCT: a statistical analysis framework for generalizing RCT findings to real-world population. JOURNAL OF BIOPHARMACEUTICAL STATISTICS, Vol. 4. https://doi.org/10.1080/10543406.2024.2333136

Yang, S., Gao, C., Zeng, D., & Wang, X. (2023, April 6). Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol. 4. https://doi.org/10.1093/jrsssb/qkad017

Gao, C., & Yang, S. (2023). Pretest estimation in combining probability and non-probability samples. ELECTRONIC JOURNAL OF STATISTICS, 17(1), 1492–1546. https://doi.org/10.1214/23-EJS2137

Liu, S., Zhang, Y., Golm, G. T., Liu, G., & Yang, S. (2023, September 26). Robust analyzes for longitudinal clinical trials with missing and non-normal continuous outcomes. STATISTICAL THEORY AND RELATED FIELDS, Vol. 9. https://doi.org/10.1080/24754269.2023.2261351

Gao, C., Yang, S., & Kim, J. K. (2023, March 2). Soft calibration for selection bias problems under mixed-effects models. BIOMETRIKA, Vol. 3. https://doi.org/10.1093/biomet/asad016

Chu, J., Lu, W., & Yang, S. (2023, March 15). Targeted optimal treatment regime learning using summary statistics. BIOMETRIKA, Vol. 3. https://doi.org/10.1093/biomet/asad020

Larsen, A., Yang, S., Reich, B. J., & Rappold, A. G. (2022). A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PM2.5 USING NUMERICAL MODEL OUTPUT. ANNALS OF APPLIED STATISTICS, 16(4), 2714–2731. https://doi.org/10.1214/22-AOAS1610

Yu, M., Lu, W., Yang, S., & Ghosh, P. (2022, August 19). A multiplicative structural nested mean model for zero-inflated outcomes. BIOMETRIKA, Vol. 8. https://doi.org/10.1093/biomet/asac050

Reich, B. J., Yang, S., & Guan, Y. (2022, March 30). Discussion on "Spatial plus : A novel approach to spatial confounding" by Dupont, Wood, and Augustin. BIOMETRICS, Vol. 3. https://doi.org/10.1111/biom.13651

Zhao, H., Zhang, X., & Yang, S. (2022, August 29). Double score matching in observational studies with multi-level treatments. COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, Vol. 8. https://doi.org/10.1080/03610918.2022.2118778

Lee, D., Yang, S., & Wang, X. (2022). Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population. JOURNAL OF CAUSAL INFERENCE, 10(1), 415–440. https://doi.org/10.1515/jci-2022-0004

Giffin, A., Gong, W., Majumder, S., Rappold, A. G., Reich, B. J., & Yang, S. (2022). Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread. SPATIAL STATISTICS, 52. https://doi.org/10.1016/j.spasta.2022.100711

Wang, J., Wong, R. K. W., Yang, S., & Chan, K. C. G. (2022). Estimation of partially conditional average treatment effect by double kernel-covariate balancing. ELECTRONIC JOURNAL OF STATISTICS, 16(2), 4332–4378. https://doi.org/10.1214/22-EJS2000

Giffin, A., Reich, B. J., Yang, S., & Rappold, A. G. (2022, September 19). Generalized propensity score approach to causal inference with spatial interference. BIOMETRICS, Vol. 9. https://doi.org/10.1111/biom.13745

Kong, D., Yang, S., & Wang, L. (2022). Identifiability of causal effects with multiple causes and a binary outcome. BIOMETRIKA, 109(1), 265–272. https://doi.org/10.1093/biomet/asab016

Lee, D., Yang, S., Dong, L., Wang, X., Zeng, D., & Cai, J. (2022, January 11). Improving trial generalizability using observational studies. BIOMETRICS, Vol. 1. https://doi.org/10.1111/biom.13609

Mao, X., Wang, Z., & Yang, S. (2022, September 19). Matrix completion under complex survey sampling. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, Vol. 9. https://doi.org/10.1007/s10463-022-00851-5

Jiang, Z., Yang, S., & Ding, P. (2022, May 20). Multiply robust estimation of causal effects under principal ignorability. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol. 5. https://doi.org/10.1111/rssb.12538

Yang, S., & Zhang, Y. (2022, April 7). Multiply robust matching estimators of average and quantile treatment effects. SCANDINAVIAN JOURNAL OF STATISTICS, Vol. 4. https://doi.org/10.1111/sjos.12585

Gao, C., Thompson, K. J., Kim, J. K., & Yang, S. (2022, May 10). Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, Vol. 5. https://doi.org/10.1111/rssa.12841

Chen, S., Yang, S., & Kim, J. K. (2022). Nonparametric Mass Imputation for Data Integration. JOURNAL OF SURVEY STATISTICS AND METHODOLOGY, 10(1), 1–24. https://doi.org/10.1093/jssam/smaa036

Zhao, H., & Yang, S. (2022). Outcome-adjusted balance measure for generalized propensity score model selection. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 221, 188–200. https://doi.org/10.1016/j.jspi.2022.04.004

Huang, M.-Y., & Yang, S. (2022). ROBUST INFERENCE OF CONDITIONAL AVERAGE TREATMENT EFFECTS USING DIMENSION REDUCTION. STATISTICA SINICA, 32, 547–567. https://doi.org/10.5705/ss.202020.0409

Yang, S. (2022). Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations. BIOMETRICS, 78(3), 937–949. https://doi.org/10.1111/biom.13471

Liu, S., Yang, S., Zhang, Y., & Liu, G. (2022, November 6). Sensitivity analyses in longitudinal clinical trials via distributional imputation. STATISTICAL METHODS IN MEDICAL RESEARCH, Vol. 11. https://doi.org/10.1177/09622802221135251

Guan, Y., Page, G. L., Reich, B. J., Ventrucci, M., & Yang, S. (2022, December 21). Spectral adjustment for spatial confounding. BIOMETRIKA, Vol. 12. https://doi.org/10.1093/biomet/asac069

Yang, S., & Kim, J. K. (2022, March 28). Statistical data integration in survey sampling: a review (vol 3, pg 625, 2020). JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE, Vol. 3. https://doi.org/10.1007/s42081-022-00152-4

Wu, L., & Yang, S. (2022, November 30). Transfer Learning of Individualized Treatment Rules from Experimental to Real-World Data. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 11. https://doi.org/10.1080/10618600.2022.2141752

Johnson, D., Pieper, K., & Yang, S. (2022, March 31). Treatment-specific marginal structural Cox model for the effect of treatment discontinuation. PHARMACEUTICAL STATISTICS, Vol. 3. https://doi.org/10.1002/pst.2211

Corder, N., & Yang, S. (2022, June 20). Utilizing stratified generalized propensity score matching to approximate blocked randomized designs with multiple treatment levels. JOURNAL OF BIOPHARMACEUTICAL STATISTICS, Vol. 6. https://doi.org/10.1080/10543406.2022.2065507

Reich, B., Yang, S., Guan, Y., Giffin, A. B., Miller, M. J., & Rappold, A. (2021). A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications. INTERNATIONAL STATISTICAL REVIEW, Vol. 5. https://doi.org/10.1111/insr.12452

Zhang, Y., Yang, S., Ye, W., Faries, D. E., Lipkovich, I., & Kadziola, Z. (2021, December 26). Practical recommendations on double score matching for estimating causal effects. STATISTICS IN MEDICINE, Vol. 12. https://doi.org/10.1002/sim.9289

Cools, F., Johnson, D., Camm, A. J., Bassand, J.-P., Verheugt, F. W. A., Yang, S., … Kakkar, A. K. (2021, July 23). Risks associated with discontinuation of oral anticoagulation in newly diagnosed patients with atrial fibrillation: Results from the GARFIELD-AF Registry. JOURNAL OF THROMBOSIS AND HAEMOSTASIS, Vol. 7. https://doi.org/10.1111/jth.15415

Yang, S., Zhang, Y., Liu, G. F., & Guan, Q. (2021, September 20). SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale. BIOMETRICS, Vol. 9. https://doi.org/10.1111/biom.13555

Dong, L., Laber, E., Goldberg, Y., Song, R., & Yang, S. (2020). Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness. STATISTICS IN MEDICINE, 39(25), 3503–3520. https://doi.org/10.1002/sim.8678

Yang, S., & Zhang, Y. (2020). Double score matching estimators of average and quantile treatment effects. Retrieved from https://arxiv.org/abs/2001.06049

Yang, S., Kim, J. K., & Song, R. (2020). Doubly robust inference when combining probability and non-probability samples with high dimensional data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 1. https://doi.org/10.1111/rssb.12354

Corder, N., & Yang, S. (2020). Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness. JOURNAL OF CAUSAL INFERENCE, 8(1), 249–271. https://doi.org/10.1515/jci-2019-0024

Corder, N., & Yang, S. (2020). Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness. Retrieved from https://arxiv.org/pdf/1905.11497

Dong, L., Yang, S., Wang, X., Zeng, D., & Cai, J. W. (2020). Integrative analysis of randomized clinicaltrials with real world evidence studies. Retrieved from https://arxiv.org/pdf/2003.01242

Li, W., Yang, S., & Han, P. (2020). Robust estimation for moment condition models with data missing not at random. Journal of Statistical Planning and Inference, 207, 246–254. https://doi.org/10.1016/j.jspi.2020.01.001

Yang, S., & Kim, J. K. (2020). [Review of Statistical data integration in survey sampling: a review]. JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE, 3(2), 625–650. https://doi.org/10.1007/s42081-020-00093-w

Yang, S., & Kim, J. K. (2020). Statistical data integration in survey sampling: a review. Retrieved from https://arxiv.org/abs/2001.03259

Yang, S., & Kim, J. K. (2019). Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework. Scandinavian Journal of Statistics, 47(3), 839–861. https://doi.org/10.1111/sjos.12429

Yang, S., Wang, L., & Ding, P. (2019). Causal inference with confounders missing not at random. Biometrika, 106(4), 875–888. https://doi.org/10.1093/biomet/asz048

Yang, S., & Ding, P. (2019). Combining Multiple Observational Data Sources to Estimate Causal Effects. Journal of the American Statistical Association, 6, 1–33. https://doi.org/10.1080/01621459.2019.1609973

Yang, S., & Zeng, D. (2019, January 2). Discussion of "Penalized Spline of Propensity Methods for Treatment Comparison" by Zhou, Elliott, and Little. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 114, pp. 31–32. https://doi.org/10.1080/01621459.2018.1537916

Yang, S. (2019). Flexible Imputation of Missing Data, 2nd ed. Journal of the American Statistical Association, 114(527), 1421–1421. https://doi.org/10.1080/01621459.2019.1662249

Mao, X., Wang, Z., & Yang, S. (2019). Matrix completion for survey data prediction with multivariate missingness. Retrieved from https://arxiv.org/pdf/1907.08360

Kong, D., Yang, S., & Wang, L. (2019). Muti-cause causal inference with unmeasured confounding and binary outcome. Retrieved from https://arxiv.org/pdf/1907.13323

Yang, S., & Kim, J. K. (2019). Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling. In Advances in Econometrics (pp. 209–234). https://doi.org/10.1108/s0731-905320190000039012

Chen, S., Yang, S., & Kim, J. K. (2019). Nonparametric mass imputation for data integration (Joint Statistical Meetings Report No. #301796).

Yang, S., Pieper, K., & Cools, F. (2019). Semiparametric estimation of structural failure time models in continuous-time processes. Biometrika, 10. https://doi.org/10.1093/biomet/asz057

Wang, Z., Kim, J. K., & Yang, S. (2018). Approximate Bayesian inference under informative sampling. BIOMETRIKA, 105(1), 91–102. https://doi.org/10.1093/biomet/asx073

Yang, S., & Ding, P. (2018). Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores. BIOMETRIKA, 105(2), 487–493. https://doi.org/10.1093/biomet/asy008

Yang, S., & Kim, J. K. (2018). Discussion: Dissecting Multiple Imputation from a Multi-phase Inference Perspective: What Happens When God's, Imputer's and Analyst's Models are Uncongenial? by X. Xie and X. L. Meng. Statistica Sinica. https://doi.org/10.5705/ss.202016.0155

Lok, J. J., Yang, S., Sharkey, B., & Hughes, M. D. (2018). Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms. LIFETIME DATA ANALYSIS, 24(2), 201–223. https://doi.org/10.1007/s10985-017-9393-4

Yang, S., Kim, J. K., & Hwang, Y. (2018). Integration of survey and big observational data for finite population inference using mass imputation. Retrieved from https://arxiv.org/abs/1807.02817

Yang, S., Tsiatis, A. A., & Blazing, M. (2018). Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach. BIOMETRICS, 74(3), 900–909. https://doi.org/10.1111/biom.12845

Yang, S. (2018). Propensity Score Weighting for Causal Inference with Clustered Data. Journal of Causal Inference, 6(2). https://doi.org/10.1515/jci-2017-0027

Yang, S., & Lok, J. J. (2018). SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS. STATISTICA SINICA, 28(4), 1703–1723. https://doi.org/10.5705/ss.202016.0133

Yang, S. (2018). Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations. Retrieved from https://arxiv.org/abs/1810.00042

Kim, J. K., & Yang, S. (2017). A note on multiple imputation under complex sampling. Biometrika, 104(1), 221–228.

Yang, S., & Kim, J. K. (2017). A semiparametric inference to regression analysis with missing covariates in survey data. Statistica Sinica, 27(1), 261–285. https://doi.org/10.5705/ss.2014.174

Yang, S., & Kim, J. K. (2017). God, devil and guru in the land of multiple imputation discussion. Statistica Sinica, 27(4), 1568–1573.

Yang, S., & Lok, J. J. (2016). A goodness-of-fit test for structural nested mean models. Biometrika, 103(3), 734–741. https://doi.org/10.1093/biomet/asw031

Yang, S., & Kim, J. K. (2016). A note on multiple imputation for method of moments estimation. Biometrika, 103(1), 244–251. https://doi.org/10.1093/biomet/asv073

Yang, S., & Kim, J. K. (2016). Fractional Imputation in Survey Sampling: A Comparative Review. Statistical Science, 31(3), 415–432. https://doi.org/10.1214/16-sts569

Oh, Y. H., & Thuente, D. J. (2016). Jamming and advanced modular-based blind rendezvous algorithms for cognitive radio networks. 2016 IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM). https://doi.org/10.1109/wowmom.2016.7523514

Yang, S., Imbens, G. W., Cui, Z., Faries, D. E., & Kadziola, Z. (2016). Propensity Score Matching and Subclassification in Observational Studies with Multi-Level Treatments. BIOMETRICS, 72(4), 1055–1065. https://doi.org/10.1111/biom.12505

Peyer, K. L., Welk, G., Bailey-Davis, L., Yang, S., & Kim, J.-K. (2015). Factors Associated with Parent Concern for Child Weight and Parenting Behaviors. Childhood Obesity, 11(3), 269–274. https://doi.org/10.1089/chi.2014.0111

Yang, S., & Kim, J. K. (2015). Likelihood-based Inference with Missing Data Under Missing-at-Random. Scandinavian Journal of Statistics, 43(2), 436–454. https://doi.org/10.1111/sjos.12184

Yang, S., & Zhu, Z. (2015). Semiparametric estimation of spectral density function for irregular spatial data. Retrieved from https://arxiv.org/abs/1508.06886

Kim, J. K., & Yang, S. (2014). Fractional hot deck imputation for robust inference under item nonresponse in survey sampling. Survey Methodology, 40(2), 211–230. Retrieved from https://lib.dr.iastate.edu/stat_las_pubs/116/

Yang, S., & Zhu, Z. (2014). Variance estimation and kriging prediction for a class of non-stationary spatial models. Statistica Sinica. https://doi.org/10.5705/ss.2013.205w

Kim, J. K., Zhu, Z., & Yang, S. (2013). Improved estimation for June Area Survey incorporating several information. Proceedings 59th ISI World Statistics Congress, 199–204.

Yang, S., Kim, J.-K., & Shin, D. W. (2013). Imputation methods for quantile estimation under missing at random. Statistics and Its Interface, 6(3), 369–377. https://doi.org/10.4310/sii.2013.v6.n3.a7

Yang, S., Kim, J.-K., & Zhu, Z. (2013). Parametric fractional imputation for mixed models with nonignorable missing data. Statistics and Its Interface, 6(3), 339–347. https://doi.org/10.4310/sii.2013.v6.n3.a4

Larsen, A., Yang, S., Rappold, A., & Reich, B. A spatial causal analysis of wildland fire- contributed PM2.5 using numerical model output. Retrieved from https://arxiv.org/pdf/2003.06037

Guan, Q., & Yang, S. A unified framework for causal inference with multiple imputation using martingale. Retrieved from https://arxiv.org/pdf/1911.04663

Tang, S., Yang, S., Wang, T., Cui, Z., Li, L., & Faries, D. Causal inference of hazard ratio based on propensity score matching. Retrieved from https://arxiv.org/pdf/1911.12430