@article{larsen_yang_reich_rappold_2022, title={A SPATIAL CAUSAL ANALYSIS OF WILDLAND FIRE-CONTRIBUTED PM2.5 USING NUMERICAL MODEL OUTPUT}, volume={16}, ISSN={["1941-7330"]}, DOI={10.1214/22-AOAS1610}, number={4}, journal={ANNALS OF APPLIED STATISTICS}, author={Larsen, Alexandra and Yang, Shu and Reich, Brian J. and Rappold, Ana G.}, year={2022}, month={Dec}, pages={2714–2731} }
 @article{yu_lu_yang_ghosh_2022, title={A multiplicative structural nested mean model for zero-inflated outcomes}, volume={8}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/asac050}, journal={BIOMETRIKA}, author={Yu, Miao and Lu, Wenbin and Yang, Shu and Ghosh, Pulak}, year={2022}, month={Aug} }
 @article{reich_yang_guan_2022, title={Discussion on "Spatial plus : A novel approach to spatial confounding" by Dupont, Wood, and Augustin}, volume={3}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13651}, journal={BIOMETRICS}, author={Reich, Brian J. and Yang, Shu and Guan, Yawen}, year={2022}, month={Mar} }
 @article{zhao_zhang_yang_2022, title={Double score matching in observational studies with multi-level treatments}, volume={8}, ISSN={["1532-4141"]}, DOI={10.1080/03610918.2022.2118778}, journal={COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION}, author={Zhao, Honghe and Zhang, Xiaofei and Yang, Shu}, year={2022}, month={Aug} }
 @article{lee_yang_wang_2022, title={Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population}, volume={10}, ISSN={["2193-3685"]}, DOI={10.1515/jci-2022-0004}, number={1}, journal={JOURNAL OF CAUSAL INFERENCE}, author={Lee, Dasom and Yang, Shu and Wang, Xiaofei}, year={2022}, month={Dec}, pages={415–440} }
 @article{giffin_gong_majumder_rappold_reich_yang_2022, title={Estimating intervention effects on infectious disease control: The effect of community mobility reduction on Coronavirus spread}, volume={52}, ISSN={["2211-6753"]}, DOI={10.1016/j.spasta.2022.100711}, journal={SPATIAL STATISTICS}, author={Giffin, Andrew and Gong, Wenlong and Majumder, Suman and Rappold, Ana G. and Reich, Brian J. and Yang, Shu}, year={2022}, month={Dec} }
 @article{giffin_reich_yang_rappold_2022, title={Generalized propensity score approach to causal inference with spatial interference}, volume={9}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13745}, journal={BIOMETRICS}, author={Giffin, A. and Reich, B. J. and Yang, S. and Rappold, A. G.}, year={2022}, month={Sep} }
 @article{kong_yang_wang_2022, title={Identifiability of causal effects with multiple causes and a binary outcome}, volume={109}, ISSN={["1464-3510"]}, DOI={10.1093/biomet/asab016}, number={1}, journal={BIOMETRIKA}, author={Kong, Dehan and Yang, Shu and Wang, Linbo}, year={2022}, month={Feb}, pages={265–272} }
 @article{lee_yang_dong_wang_zeng_cai_2022, title={Improving trial generalizability using observational studies}, volume={1}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13609}, journal={BIOMETRICS}, author={Lee, Dasom and Yang, Shu and Dong, Lin and Wang, Xiaofei and Zeng, Donglin and Cai, Jianwen}, year={2022}, month={Jan} }
 @article{mao_wang_yang_2022, title={Matrix completion under complex survey sampling}, volume={9}, ISSN={["1572-9052"]}, DOI={10.1007/s10463-022-00851-5}, journal={ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS}, author={Mao, Xiaojun and Wang, Zhonglei and Yang, Shu}, year={2022}, month={Sep} }
 @article{jiang_yang_ding_2022, title={Multiply robust estimation of causal effects under principal ignorability}, volume={5}, ISSN={["1467-9868"]}, DOI={10.1111/rssb.12538}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY}, author={Jiang, Zhichao and Yang, Shu and Ding, Peng}, year={2022}, month={May} }
 @article{yang_zhang_2022, title={Multiply robust matching estimators of average and quantile treatment effects}, volume={4}, ISSN={["1467-9469"]}, DOI={10.1111/sjos.12585}, journal={SCANDINAVIAN JOURNAL OF STATISTICS}, author={Yang, Shu and Zhang, Yunshu}, year={2022}, month={Apr} }
 @article{gao_thompson_kim_yang_2022, title={Nearest neighbour ratio imputation with incomplete multinomial outcome in survey sampling}, volume={5}, ISSN={["1467-985X"]}, DOI={10.1111/rssa.12841}, journal={JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY}, author={Gao, Chenyin and Thompson, Katherine Jenny and Kim, Jae Kwang and Yang, Shu}, year={2022}, month={May} }
 @article{chen_yang_kim_2022, title={Nonparametric Mass Imputation for Data Integration}, volume={10}, ISSN={["2325-0992"]}, DOI={10.1093/jssam/smaa036}, number={1}, journal={JOURNAL OF SURVEY STATISTICS AND METHODOLOGY}, author={Chen, Sixia and Yang, Shu and Kim, Jae Kwang}, year={2022}, month={Jan}, pages={1–24} }
 @article{zhao_yang_2022, title={Outcome-adjusted balance measure for generalized propensity score model selection}, volume={221}, ISSN={["1873-1171"]}, DOI={10.1016/j.jspi.2022.04.004}, journal={JOURNAL OF STATISTICAL PLANNING AND INFERENCE}, author={Zhao, Honghe and Yang, Shu}, year={2022}, month={Dec}, pages={188–200} }
 @article{huang_yang_2022, title={ROBUST INFERENCE OF CONDITIONAL AVERAGE TREATMENT EFFECTS USING DIMENSION REDUCTION}, volume={32}, ISSN={["1996-8507"]}, DOI={10.5705/ss.202020.0409}, journal={STATISTICA SINICA}, author={Huang, Ming-Yueh and Yang, Shu}, year={2022}, pages={547–567} }
 @article{liu_yang_zhang_liu_2022, title={Sensitivity analyses in longitudinal clinical trials via distributional imputation}, volume={11}, ISSN={["1477-0334"]}, DOI={10.1177/09622802221135251}, journal={STATISTICAL METHODS IN MEDICAL RESEARCH}, author={Liu, Siyi and Yang, Shu and Zhang, Yilong and Liu, Guanghan}, year={2022}, month={Nov} }
 @article{yang_kim_2022, title={Statistical data integration in survey sampling: a review (vol 3, pg 625, 2020)}, volume={3}, ISSN={["2520-8764"]}, DOI={10.1007/s42081-022-00152-4}, journal={JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE}, author={Yang, Shu and Kim, Jae Kwang}, year={2022}, month={Mar} }
 @article{johnson_pieper_yang_2022, title={Treatment-specific marginal structural Cox model for the effect of treatment discontinuation}, volume={3}, ISSN={["1539-1612"]}, DOI={10.1002/pst.2211}, journal={PHARMACEUTICAL STATISTICS}, author={Johnson, Dana and Pieper, Karen and Yang, Shu}, year={2022}, month={Mar} }
 @article{corder_yang_2022, title={Utilizing stratified generalized propensity score matching to approximate blocked randomized designs with multiple treatment levels}, volume={6}, ISSN={["1520-5711"]}, DOI={10.1080/10543406.2022.2065507}, journal={JOURNAL OF BIOPHARMACEUTICAL STATISTICS}, author={Corder, Nathan and Yang, Shu}, year={2022}, month={Jun} }
 @article{reich_yang_guan_giffin_miller_rappold_2021, title={A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications}, volume={5}, DOI={10.1111/insr.12452}, journal={INTERNATIONAL STATISTICAL REVIEW}, author={Reich, Brian and Yang, Shu and Guan, Yawen and Giffin, Andrew B. and Miller, Matthew J. and Rappold, Ana}, year={2021} }
 @article{zhang_yang_ye_faries_lipkovich_kadziola_2021, title={Practical recommendations on double score matching for estimating causal effects}, volume={12}, ISSN={["1097-0258"]}, DOI={10.1002/sim.9289}, journal={STATISTICS IN MEDICINE}, author={Zhang, Yunshu and Yang, Shu and Ye, Wenyu and Faries, Douglas E. and Lipkovich, Ilya and Kadziola, Zbigniew}, year={2021}, month={Dec} }
 @article{cools_johnson_camm_bassand_verheugt_yang_tsiatis_fitzmaurice_goldhaber_kayani_et al._2021, title={Risks associated with discontinuation of oral anticoagulation in newly diagnosed patients with atrial fibrillation: Results from the GARFIELD-AF Registry}, volume={7}, ISSN={["1538-7836"]}, DOI={10.1111/jth.15415}, journal={JOURNAL OF THROMBOSIS AND HAEMOSTASIS}, author={Cools, Frank and Johnson, Dana and Camm, Alan J. and Bassand, Jean-Pierre and Verheugt, Freek W. A. and Yang, Shu and Tsiatis, Anastasios and Fitzmaurice, David A. and Goldhaber, Samuel Z. and Kayani, Gloria and et al.}, year={2021}, month={Jul} }
 @article{yang_zhang_liu_guan_2021, title={SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale}, volume={9}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13555}, journal={BIOMETRICS}, author={Yang, Shu and Zhang, Yilong and Liu, Guanghan Frank and Guan, Qian}, year={2021}, month={Sep} }
 @article{yang_2021, title={Semiparametric estimation of structural nested mean models with irregularly spaced longitudinal observations}, ISBN={1541-0420}, DOI={10.1111/biom.13471}, journal={BIOMETRICS}, author={Yang, Shu}, year={2021} }
 @article{dong_laber_goldberg_song_yang_2020, title={Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness}, ISBN={1097-0258}, DOI={10.1002/sim.8678}, journal={STATISTICS IN MEDICINE}, author={Dong, Lin and Laber, Eric and Goldberg, Yair and Song, Rui and Yang, Shu}, year={2020} }
 @book{yang_zhang_2020, title={Double score matching estimators of average and quantile treatment effects}, url={https://arxiv.org/abs/2001.06049}, author={Yang, S. and Zhang, Y.}, year={2020} }
 @article{yang_kim_song_2020, title={Doubly robust inference when combining probability and non-probability samples with high dimensional data}, volume={1}, ISSN={1369-7412}, url={http://dx.doi.org/10.1111/rssb.12354}, DOI={10.1111/rssb.12354}, journal={Journal of the Royal Statistical Society: Series B (Statistical Methodology)}, publisher={Wiley}, author={Yang, Shu and Kim, Jae Kwang and Song, Rui}, year={2020}, month={Jan} }
 @book{corder_yang_2020, title={Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness}, url={https://arxiv.org/pdf/1905.11497}, author={Corder, N. and Yang, S.}, year={2020} }
 @article{corder_yang_2020, title={Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness}, volume={8}, ISBN={2193-3685}, DOI={10.1515/jci-2019-0024}, number={1}, journal={JOURNAL OF CAUSAL INFERENCE}, author={Corder, Nathan and Yang, Shu}, year={2020}, month={Jan}, pages={249–271} }
 @book{dong_yang_wang_zeng_cai_2020, title={Integrative analysis of randomized clinicaltrials with real world evidence studies}, url={https://arxiv.org/pdf/2003.01242}, author={Dong, L. and Yang, S. and Wang, X. and Zeng, D. and Cai, J.W.}, year={2020} }
 @article{li_yang_han_2020, title={Robust estimation for moment condition models with data missing not at random}, volume={207}, ISBN={1873-1171}, DOI={10.1016/j.jspi.2020.01.001}, journal={Journal of Statistical Planning and Inference}, author={Li, W. and Yang, S. and Han, P.}, year={2020}, month={Jul}, pages={246–254} }
 @book{yang_kim_2020, title={Statistical data integration in survey sampling: a review}, url={https://arxiv.org/abs/2001.03259}, author={Yang, S. and Kim, J.K.}, year={2020}, month={Jan} }
 @misc{yang_kim_2020, title={Statistical data integration in survey sampling: a review}, volume={3}, ISSN={["2520-8764"]}, DOI={10.1007/s42081-020-00093-w}, number={2}, journal={JAPANESE JOURNAL OF STATISTICS AND DATA SCIENCE}, author={Yang, Shu and Kim, Jae Kwang}, year={2020}, month={Dec}, pages={625–650} }
 @article{yang_kim_2019, title={Asymptotic theory and inference of predictive mean matching imputation using a superpopulation model framework}, ISBN={1467-9469}, ISSN={0303-6898 1467-9469}, url={http://dx.doi.org/10.1111/sjos.12429}, DOI={10.1111/sjos.12429}, journal={Scandinavian Journal of Statistics}, publisher={Wiley}, author={Yang, Shu and Kim, Jae Kwang}, year={2019}, month={Dec} }
 @article{yang_wang_ding_2019, title={Causal inference with confounders missing not at random}, volume={106}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/asz048}, DOI={10.1093/biomet/asz048}, abstractNote={Summary
               <jats:p>It is important to draw causal inference from observational studies, but this becomes challenging if the confounders have missing values. Generally, causal effects are not identifiable if the confounders are missing not at random. In this article we propose a novel framework for nonparametric identification of causal effects with confounders subject to an outcome-independent missingness, which means that the missing data mechanism is independent of the outcome, given the treatment and possibly missing confounders. We then propose a nonparametric two-stage least squares estimator and a parametric estimator for causal effects.}, number={4}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Yang, S and Wang, L and Ding, P}, year={2019}, month={Sep}, pages={875–888} }
 @article{yang_ding_2019, title={Combining Multiple Observational Data Sources to Estimate Causal Effects}, volume={6}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.2019.1609973}, DOI={10.1080/01621459.2019.1609973}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Yang, Shu and Ding, Peng}, year={2019}, month={Jun}, pages={1–33} }
 @article{yang_zeng_2019, title={Discussion of "Penalized Spline of Propensity Methods for Treatment Comparison" by Zhou, Elliott, and Little}, volume={114}, ISBN={1537-274X}, DOI={10.1080/01621459.2018.1537916}, number={525}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Yang, Shu and Zeng, Donglin}, year={2019}, pages={31–32} }
 @article{yang_2019, title={Flexible Imputation of Missing Data, 2nd ed.}, volume={114}, ISSN={0162-1459 1537-274X}, url={http://dx.doi.org/10.1080/01621459.2019.1662249}, DOI={10.1080/01621459.2019.1662249}, number={527}, journal={Journal of the American Statistical Association}, publisher={Informa UK Limited}, author={Yang, Shu}, year={2019}, month={Jul}, pages={1421–1421} }
 @book{mao_wang_yang_2019, title={Matrix completion for survey data prediction with multivariate missingness}, url={https://arxiv.org/pdf/1907.08360}, author={Mao, X. and Wang, Z. and Yang, S.}, year={2019}, month={Aug} }
 @book{kong_yang_wang_2019, title={Muti-cause causal inference with unmeasured confounding and binary outcome}, url={https://arxiv.org/pdf/1907.13323}, author={Kong, D. and Yang, S. and Wang, L.}, year={2019}, month={Jul} }
 @inbook{yang_kim_2019, title={Nearest Neighbor Imputation for General Parameter Estimation in Survey Sampling}, ISBN={9781787567269 9781787567252}, ISSN={0731-9053}, url={http://dx.doi.org/10.1108/s0731-905320190000039012}, DOI={10.1108/s0731-905320190000039012}, booktitle={Advances in Econometrics}, publisher={Emerald Publishing Limited}, author={Yang, Shu and Kim, Jae Kwang}, year={2019}, month={Mar}, pages={209–234} }
 @book{chen_yang_kim_2019, title={Nonparametric mass imputation for data integration}, number={#301796}, author={Chen, S. and Yang, S. and Kim, J.K.}, year={2019} }
 @article{yang_pieper_cools_2019, title={Semiparametric estimation of structural failure time models in continuous-time processes}, volume={10}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/asz057}, DOI={10.1093/biomet/asz057}, abstractNote={Summary
               <jats:p>Structural failure time models are causal models for estimating the effect of time-varying treatments on a survival outcome. G-estimation and artificial censoring have been proposed for estimating the model parameters in the presence of time-dependent confounding and administrative censoring. However, most existing methods require manually pre-processing data into regularly spaced data, which may invalidate the subsequent causal analysis. Moreover, the computation and inference are challenging due to the nonsmoothness of artificial censoring. We propose a class of continuous-time structural failure time models that respects the continuous-time nature of the underlying data processes. Under a martingale condition of no unmeasured confounding, we show that the model parameters are identifiable from a potentially infinite number of estimating equations. Using the semiparametric efficiency theory, we derive the first semiparametric doubly robust estimators, which are consistent if the model for the treatment process or the failure time model, but not necessarily both, is correctly specified. Moreover, we propose using inverse probability of censoring weighting to deal with dependent censoring. In contrast to artificial censoring, our weighting strategy does not introduce nonsmoothness in estimation and ensures that resampling methods can be used for inference.}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Yang, S and Pieper, K and Cools, F}, year={2019}, month={Oct} }
 @article{wang_kim_yang_2018, title={Approximate Bayesian inference under informative sampling}, volume={105}, DOI={10.1093/biomet/asx073}, number={1}, journal={Biometrika}, author={Wang, Z. and Kim, J. K. and Yang, Shu}, year={2018}, pages={91–102} }
 @article{yang_ding_2018, title={Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores}, volume={105}, DOI={10.1093/biomet/asy008}, number={2}, journal={Biometrika}, author={Yang, Shu and Ding, P.}, year={2018}, pages={487–493} }
 @article{yang_kim_2018, title={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}, ISSN={1017-0405}, url={http://dx.doi.org/10.5705/ss.202016.0155}, DOI={10.5705/ss.202016.0155}, journal={Statistica Sinica}, publisher={Institute of Statistical Science}, author={Yang, Shu and Kim, Jae Kwang}, year={2018} }
 @article{lok_yang_sharkey_hughes_2018, title={Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms}, volume={24}, DOI={10.1007/s10985-017-9393-4}, number={2}, journal={Lifetime Data Analysis}, author={Lok, J. J. and Yang, Shu and Sharkey, B. and Hughes, M. D.}, year={2018}, pages={201–223} }
 @book{yang_kim_hwang_2018, title={Integration of survey and big observational data for finite population inference using mass imputation}, url={https://arxiv.org/abs/1807.02817}, author={Yang, S. and Kim, J.K. and Hwang, Youngdeok}, year={2018}, month={Jul} }
 @article{yang_tsiatis_blazing_2018, title={Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach}, volume={74}, DOI={10.1111/biom.12845}, number={3}, journal={BIOMETRICS}, author={Yang, Shu and Tsiatis, Anastasios A. and Blazing, Michael}, year={2018}, pages={900–909} }
 @article{yang_2018, title={Propensity Score Weighting for Causal Inference with Clustered Data}, volume={6}, ISSN={2193-3685}, url={http://dx.doi.org/10.1515/jci-2017-0027}, DOI={10.1515/jci-2017-0027}, abstractNote={Abstract
Propensity score weighting is a tool for causal inference to adjust for measured confounders in observational studies. In practice, data often present complex structures, such as clustering, which make propensity score modeling and estimation challenging. In addition, for clustered data, there may be unmeasured cluster-level covariates that are related to both the treatment assignment and outcome. When such unmeasured cluster-specific confounders exist and are omitted in the propensity score model, the subsequent propensity score adjustment may be biased. In this article, we propose a calibration technique for propensity score estimation under the latent ignorable treatment assignment mechanism, i. e., the treatment-outcome relationship is unconfounded given the observed covariates and the latent cluster-specific confounders. We impose novel balance constraints which imply exact balance of the observed confounders and the unobserved cluster-level confounders between the treatment groups. We show that the proposed calibrated propensity score weighting estimator is doubly robust in that it is consistent for the average treatment effect if either the propensity score model is correctly specified or the outcome follows a linear mixed effects model. Moreover, the proposed weighting method can be combined with sampling weights for an integrated solution to handle confounding and sampling designs for causal inference with clustered survey data. In simulation studies, we show that the proposed estimator is superior to other competitors. We estimate the effect of School Body Mass Index Screening on prevalence of overweight and obesity for elementary schools in Pennsylvania.}, number={2}, journal={Journal of Causal Inference}, publisher={Walter de Gruyter GmbH}, author={Yang, Shu}, year={2018}, month={Sep} }
 @article{yang_lok_2018, title={SENSITIVITY ANALYSIS FOR UNMEASURED CONFOUNDING IN COARSE STRUCTURAL NESTED MEAN MODELS}, volume={28}, DOI={10.5705/ss.202016.0133}, number={4}, journal={STATISTICA SINICA}, author={Yang, Shu and Lok, Judith J.}, year={2018}, pages={1703–1723} }
 @article{yang_2018, title={Semiparametric efficient estimation of structural nested mean models with irregularly spaced observations}, url={https://arxiv.org/abs/1810.00042}, author={Yang, S.}, year={2018}, month={Jan} }
 @article{kim_yang_2017, title={A note on multiple imputation under complex sampling}, volume={104}, number={1}, journal={Biometrika}, author={Kim, J. K. and Yang, S.}, year={2017}, pages={221–228} }
 @article{yang_kim_2017, title={A semiparametric inference to regression analysis with missing covariates in survey data}, volume={27}, ISSN={1017-0405}, url={http://dx.doi.org/10.5705/ss.2014.174}, DOI={10.5705/ss.2014.174}, number={1}, journal={Statistica Sinica}, publisher={Institute of Statistical Science}, author={Yang, Shu and Kim, Jae Kwang}, year={2017}, pages={261–285} }
 @article{yang_kim_2017, title={God, devil and guru in the land of multiple imputation discussion}, volume={27}, number={4}, journal={Statistica Sinica}, author={Yang, S. and Kim, J. K.}, year={2017}, pages={1568–1573} }
 @article{yang_lok_2016, title={A goodness-of-fit test for structural nested mean models}, volume={103}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/asw031}, DOI={10.1093/biomet/asw031}, number={3}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Yang, S. and Lok, J. J.}, year={2016}, month={Jul}, pages={734–741} }
 @article{yang_kim_2016, title={A note on multiple imputation for method of moments estimation}, volume={103}, ISSN={0006-3444 1464-3510}, url={http://dx.doi.org/10.1093/biomet/asv073}, DOI={10.1093/biomet/asv073}, number={1}, journal={Biometrika}, publisher={Oxford University Press (OUP)}, author={Yang, S. and Kim, J. K.}, year={2016}, month={Feb}, pages={244–251} }
 @article{yang_kim_2016, title={Fractional Imputation in Survey Sampling: A Comparative Review}, volume={31}, ISSN={0883-4237}, url={http://dx.doi.org/10.1214/16-sts569}, DOI={10.1214/16-sts569}, number={3}, journal={Statistical Science}, publisher={Institute of Mathematical Statistics}, author={Yang, Shu and Kim, Jae Kwang}, year={2016}, month={Aug}, pages={415–432} }
 @inproceedings{oh_thuente_2016, title={Jamming and advanced modular-based blind rendezvous algorithms for cognitive radio networks}, DOI={10.1109/wowmom.2016.7523514}, booktitle={2016 IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM)}, author={Oh, Y. H. and Thuente, D. J.}, year={2016} }
 @article{yang_imbens_cui_faries_kadziola_2016, title={Propensity score matching and subclassification in observational studies with multi-level treatments}, volume={72}, DOI={10.1111/biom.12505}, number={4}, journal={Biometrics}, author={Yang, Shu and Imbens, G. W. and Cui, Z. L. and Faries, D. E. and Kadziola, Z.}, year={2016}, pages={1055–1065} }
 @article{peyer_welk_bailey-davis_yang_kim_2015, title={Factors Associated with Parent Concern for Child Weight and Parenting Behaviors}, volume={11}, ISSN={2153-2168 2153-2176}, url={http://dx.doi.org/10.1089/chi.2014.0111}, DOI={10.1089/chi.2014.0111}, number={3}, journal={Childhood Obesity}, publisher={Mary Ann Liebert Inc}, author={Peyer, Karissa L. and Welk, Gregory and Bailey-Davis, Lisa and Yang, Shu and Kim, Jae-Kwang}, year={2015}, month={Jun}, pages={269–274} }
 @article{yang_kim_2015, title={Likelihood-based Inference with Missing Data Under Missing-at-Random}, volume={43}, ISSN={0303-6898}, url={http://dx.doi.org/10.1111/sjos.12184}, DOI={10.1111/sjos.12184}, number={2}, journal={Scandinavian Journal of Statistics}, publisher={Wiley}, author={Yang, Shu and Kim, Jae Kwang}, year={2015}, month={Oct}, pages={436–454} }
 @book{yang_zhu_2015, title={Semiparametric estimation of spectral density function for irregular spatial data}, url={https://arxiv.org/abs/1508.06886}, author={Yang, S. and Zhu, Z.}, year={2015} }
 @article{kim_yang_2014, title={Fractional hot deck imputation for robust inference under item nonresponse in survey sampling}, volume={40}, url={https://lib.dr.iastate.edu/stat_las_pubs/116/}, number={2}, journal={Survey Methodology}, author={Kim, J.K. and Yang, S.}, year={2014}, month={Dec}, pages={211–230} }
 @article{yang_zhu_2014, title={Variance estimation and kriging prediction for a class of non-stationary spatial models}, ISSN={1017-0405}, url={http://dx.doi.org/10.5705/ss.2013.205w}, DOI={10.5705/ss.2013.205w}, journal={Statistica Sinica}, publisher={Institute of Statistical Science}, author={Yang, Shu and Zhu, Zhengyuan}, year={2014} }
 @inproceedings{kim_zhu_yang_2013, title={Improved estimation for June Area Survey incorporating several information}, booktitle={Proceedings 59th ISI World Statistics Congress}, author={Kim, J.K. and Zhu, Z. and Yang, S.}, year={2013}, pages={199–204} }
 @article{yang_kim_shin_2013, title={Imputation methods for quantile estimation under missing at random}, volume={6}, ISSN={1938-7989 1938-7997}, url={http://dx.doi.org/10.4310/sii.2013.v6.n3.a7}, DOI={10.4310/sii.2013.v6.n3.a7}, number={3}, journal={Statistics and Its Interface}, publisher={International Press of Boston}, author={Yang, Shu and Kim, Jae-Kwang and Shin, Dong Wan}, year={2013}, pages={369–377} }
 @article{yang_kim_zhu_2013, title={Parametric fractional imputation for mixed models with nonignorable missing data}, volume={6}, ISSN={1938-7989 1938-7997}, url={http://dx.doi.org/10.4310/sii.2013.v6.n3.a4}, DOI={10.4310/sii.2013.v6.n3.a4}, number={3}, journal={Statistics and Its Interface}, publisher={International Press of Boston}, author={Yang, Shu and Kim, Jae-Kwang and Zhu, Zhengyuan}, year={2013}, pages={339–347} }
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