Journal Articles
30. Minimax semiparametric learning with approximate sparsity | 2019+
Root-n optimality of double robustness under approximate sparsity
that illustrates differences from classical semiparametrics
(with Victor Chernozhukov, Whitney K. Newey and Yinchu Zhu)
29. Detangling robustness in high-dimensions: composite vs model-averaged estimation | 2019+
Illustrated that composite estimation has benefits over model-averaged
estimation in high-dimensions with sparsity
(with Jing Zhou and Gerda Claeskens)
28. Sparsity double robust inference of average treatment effects | 2019+
Proposed new balancing via moment-targeting for average
treatment effect estimation and inference
(with Stefan Wager and Yinchu Zhu)
27. Causal quantile learner: robust structural equations | 2019+
Proposed quantile invariance for learning cause-effect relationship between many
variables with interventional and observational data
(with Denise Rava)
26. Synthetic learner: model-free inference on treatments over time | 2019+
Inference for counterfactuals using machine learning
where the true model may not be captured in the dictionary
(with Davide Viviano)
25. Estimating treatment effects under additive hazards models with high-dimensional covariates | JASA:T&M, revision
New score developed for confidence intervals of treatment effects
in the presence of censored outcomes
(with Jue Hou and Ronghui Xu)
24. Censored quantile regression forest | AIStats, to appear
Censored quantile random forest estimates
quantiles of censored responses in a regression setting non-parametrically.
(with Hanbo Li )
23. High-dimensional semi-supervised learning: in search of optimal inference of the mean | Biometrika, major revision
Semi-supervised inference of the responses mean when covariates
are high-dimensional and model is not necessarily correctly specified
(with Yuqian Zhang)
22. Tuning free robust and efficient approach to high-dimensional regression | JASA:T&M, major revision
Variable selection without tuning that is robust as well as efficient
(with Lan Wang, Runze Li, Bo Peng and Yunan Wu )
21. Confidence intervals for high-dimensional Cox models | Statistica SINICA, to appear
Assumption-lean asymptotic theory for the inference in the Cox model
(with Yi Yu and Richard J. Samworth )
20. Minimax rates and adaptivity of tests in high-dimensional linear models with non-sparse structures | AOS, to appear
New minimax optimality results regarding confidence intervals -- no sparsity restrictions needed
(with Jianqing Fan and Yinchu Zhu )
19. Asymptotic theory of rank estimation for high-dimensional accelerated failure time models | AOS, revision
Finite-sample estimation properties of new regularizer for AFT models
(with Lan Wang )
18. High-dimensional classification with errors in variables using high-confidence sets | 2018+
Classifiers for noisy data with possibly non-sparse classification boundaries
(with Emre Barut, Jianqing Fan and Jiancheng Jiang )
17. Inference under Fine-Gray competing risks model with high-dimensional covariates | EJS, 13(2), 4449-4507, (2019)
Regularization and testing for Fine-Gray model with many more parameters than samples
(with Jue Hou and Ronghui Xu )
16. Testing fixed effects in high-dimensional misspecified linear mixed models | JASA:T&M, to appear
Estimators and tests adaptive to misspecification of random effects
(with Gerda Claeskens and Thomas Gueuning )
15. Breaking the curse of dimensionality in high-dimensions | JMLR, revision
Tests of multivariate parameters in high-dimensional setting
that does not rely on the sparsity of the underlying linear model
(with Yinchu Zhu)
14. A projection pursuit framework for testing general high-dimensional hypothesis | 2017+
New projection estimator and test statistic based on the contrast of two competing estimators
(with Yinchu Zhu)
13. Two-sample testing in high-dimensional and dense models | 2016+
New algorithm TIERS for distinguishing between coefficients of two
high-dimensional regressions
(with Yinchu Zhu)
12. Uniform inference for high-dimensional quantile process: linear testing and regression rank scores | AOS, revision
Uniform bahadur representation, dual problems and uniform multivariate tests
(with Mladen Kolar)
11. Generalized M-estimators for high-dimensional tobit I models | EJS, 13(1), 582-645, (2019)
Mallow's, Hill-Ryan's and Sweepe's one-step estimators for
left (fixed) censored data: Tobit I model and its variants
(with Jiaqi Guo)
10. Linear hypothesis testing in dense high-dimensional linear models | JASA:T&M, 113(524), 1583-1600, (2018)
New restructured regression method for testing hypothesis with
dense parameters and dense loadings in high-dimensions
(with Yinchu Zhu)
9. Significance testing in non-sparse high-dimensional linear models | EJS, 12(2), 3312-3364 (2018)
New method CorrT proposed that preserves Type I error and Type II error even in
dense (non-sparse) and ultra high-dimensional models
(with Yinchu Zhu)
8. Boosting in the presence of outliers: adaptive classification in the presence of outliers | JASA:T&M , 113(512), 660-674, (2018)
New boosting method ArchBoost that is robust to data or label perturbations
-- adversarial or not
(with Alexander Hanbo Li)
7. Comment on "High dimensional simultaneous inference via bootstrap"
by R. Dezeure, P. Buhlmann and C-H. Zhang | TEST, 26(4), 720-728 (2017)
Discussed residual bootstrap efficiency and proposed new residual bootstrap
for mixture of sparse and dense models
(with Yinchu Zhu)
6. Robustness in sparse high-dimensional models:
relative efficiency based on approximate message passing | EJS, 10(2), 3894-3944 (2016)
New AMP algorithm is proposed, RAMP, that is shown to be
efficient regardless of the error distribution
5. Randomized maximum contrast selection: subagging for large-scale regression | EJS, 10(1), 121-170, (2016)
Model selection for big data: naive selection of variables fails
whereas maximum contrast selection succeeds
4. Cultivating disaster donors using data analytics | Mgmt. Science, 62(3), 849-866, (2016)
Importance sampling and logistic regression in
divide and conquer setting
(with Ilya Ryzhov and Bin Han)
3. Structured estimation in non-parametric Cox model | EJS, 9(1), 492-534, (2015)
Estimation in misspecified high-dimensional Cox model
(with Rui Song)
2. Regularization for Cox's proportional hazard model with NP dimensionality | AOS, 39(6), 3092-3120, (2011)
Lasso and SCAD model selection properties for ultra high dimensional data
(with Jianqing Fan and Jiancheng Jiang)
1. Composite quasi-likelihood for high-dimensional variable selection | JRSSB, 73(3), 325-349, (2011)
Model selection robust and adaptive to the error distribution
(with Jianqing Fan and Weiwei Wang)
Root-n optimality of double robustness under approximate sparsity
that illustrates differences from classical semiparametrics
(with Victor Chernozhukov, Whitney K. Newey and Yinchu Zhu)
29. Detangling robustness in high-dimensions: composite vs model-averaged estimation | 2019+
Illustrated that composite estimation has benefits over model-averaged
estimation in high-dimensions with sparsity
(with Jing Zhou and Gerda Claeskens)
28. Sparsity double robust inference of average treatment effects | 2019+
Proposed new balancing via moment-targeting for average
treatment effect estimation and inference
(with Stefan Wager and Yinchu Zhu)
27. Causal quantile learner: robust structural equations | 2019+
Proposed quantile invariance for learning cause-effect relationship between many
variables with interventional and observational data
(with Denise Rava)
26. Synthetic learner: model-free inference on treatments over time | 2019+
Inference for counterfactuals using machine learning
where the true model may not be captured in the dictionary
(with Davide Viviano)
25. Estimating treatment effects under additive hazards models with high-dimensional covariates | JASA:T&M, revision
New score developed for confidence intervals of treatment effects
in the presence of censored outcomes
(with Jue Hou and Ronghui Xu)
24. Censored quantile regression forest | AIStats, to appear
Censored quantile random forest estimates
quantiles of censored responses in a regression setting non-parametrically.
(with Hanbo Li )
23. High-dimensional semi-supervised learning: in search of optimal inference of the mean | Biometrika, major revision
Semi-supervised inference of the responses mean when covariates
are high-dimensional and model is not necessarily correctly specified
(with Yuqian Zhang)
22. Tuning free robust and efficient approach to high-dimensional regression | JASA:T&M, major revision
Variable selection without tuning that is robust as well as efficient
(with Lan Wang, Runze Li, Bo Peng and Yunan Wu )
21. Confidence intervals for high-dimensional Cox models | Statistica SINICA, to appear
Assumption-lean asymptotic theory for the inference in the Cox model
(with Yi Yu and Richard J. Samworth )
20. Minimax rates and adaptivity of tests in high-dimensional linear models with non-sparse structures | AOS, to appear
New minimax optimality results regarding confidence intervals -- no sparsity restrictions needed
(with Jianqing Fan and Yinchu Zhu )
19. Asymptotic theory of rank estimation for high-dimensional accelerated failure time models | AOS, revision
Finite-sample estimation properties of new regularizer for AFT models
(with Lan Wang )
18. High-dimensional classification with errors in variables using high-confidence sets | 2018+
Classifiers for noisy data with possibly non-sparse classification boundaries
(with Emre Barut, Jianqing Fan and Jiancheng Jiang )
17. Inference under Fine-Gray competing risks model with high-dimensional covariates | EJS, 13(2), 4449-4507, (2019)
Regularization and testing for Fine-Gray model with many more parameters than samples
(with Jue Hou and Ronghui Xu )
16. Testing fixed effects in high-dimensional misspecified linear mixed models | JASA:T&M, to appear
Estimators and tests adaptive to misspecification of random effects
(with Gerda Claeskens and Thomas Gueuning )
15. Breaking the curse of dimensionality in high-dimensions | JMLR, revision
Tests of multivariate parameters in high-dimensional setting
that does not rely on the sparsity of the underlying linear model
(with Yinchu Zhu)
14. A projection pursuit framework for testing general high-dimensional hypothesis | 2017+
New projection estimator and test statistic based on the contrast of two competing estimators
(with Yinchu Zhu)
13. Two-sample testing in high-dimensional and dense models | 2016+
New algorithm TIERS for distinguishing between coefficients of two
high-dimensional regressions
(with Yinchu Zhu)
12. Uniform inference for high-dimensional quantile process: linear testing and regression rank scores | AOS, revision
Uniform bahadur representation, dual problems and uniform multivariate tests
(with Mladen Kolar)
11. Generalized M-estimators for high-dimensional tobit I models | EJS, 13(1), 582-645, (2019)
Mallow's, Hill-Ryan's and Sweepe's one-step estimators for
left (fixed) censored data: Tobit I model and its variants
(with Jiaqi Guo)
10. Linear hypothesis testing in dense high-dimensional linear models | JASA:T&M, 113(524), 1583-1600, (2018)
New restructured regression method for testing hypothesis with
dense parameters and dense loadings in high-dimensions
(with Yinchu Zhu)
9. Significance testing in non-sparse high-dimensional linear models | EJS, 12(2), 3312-3364 (2018)
New method CorrT proposed that preserves Type I error and Type II error even in
dense (non-sparse) and ultra high-dimensional models
(with Yinchu Zhu)
8. Boosting in the presence of outliers: adaptive classification in the presence of outliers | JASA:T&M , 113(512), 660-674, (2018)
New boosting method ArchBoost that is robust to data or label perturbations
-- adversarial or not
(with Alexander Hanbo Li)
7. Comment on "High dimensional simultaneous inference via bootstrap"
by R. Dezeure, P. Buhlmann and C-H. Zhang | TEST, 26(4), 720-728 (2017)
Discussed residual bootstrap efficiency and proposed new residual bootstrap
for mixture of sparse and dense models
(with Yinchu Zhu)
6. Robustness in sparse high-dimensional models:
relative efficiency based on approximate message passing | EJS, 10(2), 3894-3944 (2016)
New AMP algorithm is proposed, RAMP, that is shown to be
efficient regardless of the error distribution
5. Randomized maximum contrast selection: subagging for large-scale regression | EJS, 10(1), 121-170, (2016)
Model selection for big data: naive selection of variables fails
whereas maximum contrast selection succeeds
4. Cultivating disaster donors using data analytics | Mgmt. Science, 62(3), 849-866, (2016)
Importance sampling and logistic regression in
divide and conquer setting
(with Ilya Ryzhov and Bin Han)
3. Structured estimation in non-parametric Cox model | EJS, 9(1), 492-534, (2015)
Estimation in misspecified high-dimensional Cox model
(with Rui Song)
2. Regularization for Cox's proportional hazard model with NP dimensionality | AOS, 39(6), 3092-3120, (2011)
Lasso and SCAD model selection properties for ultra high dimensional data
(with Jianqing Fan and Jiancheng Jiang)
1. Composite quasi-likelihood for high-dimensional variable selection | JRSSB, 73(3), 325-349, (2011)
Model selection robust and adaptive to the error distribution
(with Jianqing Fan and Weiwei Wang)