Wenbin Lu

Editorial to the special issue ‘Nonparametric Methods in Data Science and Applications’

Zhao, Y., & Lu, W. (2025, October 2). Editorial to the special issue ‘Nonparametric Methods in Data Science and Applications.’ Journal of Nonparametric Statistics.

Efficient causal decision making with one-sided feedback

International Conference on Learning Representations (ICLR 2025). Presented at the International Conference on Learning Representations.

Evaluating and learning optimal dynamic treatment regimes under truncation by death

Neural Information Processing Systems (NeurIPS 2025). Presented at the Neural Information Processing Systems.

Linear contextual bandits with interference

International Conference on Machine Learning (ICML 2025). Presented at the International Conference on Machine Learning.

Multivariate zero-inflated causal model for regional mobility restriction effects on consumer spending

Hong, T., Lu, W., Yang, S., & Ghosh, P. (2025, January 1). Journal of Causal Inference.

Off-policy evaluation under nonignorable missing data

International Conference on Machine Learning (ICML 2025). Presented at the International Conference on Machine Learning.

A shadow variable approach to causal decision making with one-sided feedback

NeurIPS 2024 Causal Representation Learning Workshop. Presented at the NeurIPS 2024 Causal Representation Learning Workshop.

Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator

Rhodes, G., Davidian, M., & Lu, W. (2024, February 9). Biostatistics, Vol. 2.

Methods for the estimation of direct and indirect vaccination effects by combining data from individual‐ and cluster‐randomized trials

Wang, R., Cen, M., Huang, Y., Qian, G., Dean, N. E., Ellenberg, S. S., … Longini, I. M. (2024, February 13). Statistics in Medicine, Vol. 2.

Distributed community detection in large networks

Journal of Machine Learning Research, 24(401), 1–28.

Dynamic prediction of residual life with longitudinal covariates using long short-term memory networks

Rhodes, G., Davidian, M., & Lu, W. (2023, September 1). The Annals of Applied Statistics, Vol. 17, pp. 2039–2058.

Jump interval-learning for individualized decision making with continuous treatments

Journal of Machine Learning Research, 24(140), 1–92.

Mining the factor zoo: Estimation of latent factor models with sufficient proxies

Wan, R., Li, Y., Lu, W., & Song, R. (2023, February 3). Journal of Econometrics, Vol. 239.

Multiple robust off-policy evaluation and learning under truncation by death

International Conference on Machine Learning (ICML2023). Presented at the International Conference on Machine Learning.

On Learning and Testing of Counterfactual Fairness through Data Preprocessing

Chen, H., Lu, W., Song, R., & Ghosh, P. (2023, March 6). Journal of the American Statistical Association, Vol. 4.

Targeted optimal treatment regime learning using summary statistics

Chu, J., Lu, W., & Yang, S. (2023, March 15). Biometrika, Vol. 3.

TransModel: An R Package for Linear Transformation Model with Censored Data

Zhou, J., Zhang, J., & Lu, W. (2022, January 1). Journal of Statistical Software, Vol. 101.

A General Framework for Subgroup Detection via One-Step Value Difference Estimation

Johnson, D., Lu, W., & Davidian, M. (2022, July 6). Biometrics, Vol. 8.

A multiplicative structural nested mean model for zero-inflated outcomes

Yu, M., Lu, W., Yang, S., & Ghosh, P. (2022, August 19). Biometrika, Vol. 8.

CAPITAL: Optimal subgroup identification via constrained policy tree search

Cai, H., Lu, W., West, R. M., Mehrotra, D. V., & Huang, L. (2022, July 7). Statistics in Medicine, Vol. 7.

Change-Plane Analysis for Subgroup Detection with a Continuous Treatment

Jin, P., Lu, W., Chen, Y., & Liu, M. (2022, September 22). Biometrics, Vol. 10.

Distributed Cox proportional hazards regression using summary-level information

Li, D., Lu, W., Shu, D., Toh, S., & Wang, R. (2022, February 1). Biostatistics, Vol. 2.

Functional Data Analysis for Longitudinal Data with Informative Observation Times

Weaver, C., Xiao, L., & Lu, W. (2022, February 21). Biometrics, Vol. 3.

Marginal Proportional Hazards Models for Clustered Interval-Censored Data with Time-Dependent Covariates

Cook, K., Lu, W., & Wang, R. (2022, October 31). Biometrics, Vol. 12.

Phylogeny-guided microbiome OTU-specific association test (POST)

Huang, C., Callahan, B. J., Wu, M. C., Holloway, S. T., Brochu, H., Lu, W., … Tzeng, J.-Y. (2022, June 7). Microbiome, Vol. 6.

A Probit Tensor Factorization Model For Relational Learning

Liu, Y., Song, R., Lu, W., & Xiao, Y. (2021, November 18). Journal of Computational and Graphical Statistics, Vol. 3.

Concordance and Value Information Criteria for Optimal Treatment Decision

Annals of Statistics, 49(1), 49–75.

Efficient Estimation for Dimension Reduction with Censored Survival Data

Zhao, G., Ma, Y., & Lu, W. (2021, February 18). Statistica Sinica, Vol. 32, pp. 2359–2380.

GEAR: On optimal decision making with auxiliary data

Cai, H., Song, R., & Lu, W. (2021, June 29). Stat, Vol. 12.

Gene-set integrative analysis of multi-omics data using tensor-based association test

Chang, S.-M., Yang, M., Lu, W., Huang, Y.-J., Huang, Y., Hung, H., … Tzeng, J.-Y. (2021, February 24). (A. Valencia, Ed.). Bioinformatics, Vol. 37, pp. 2259–2265.

On estimating optimal regime for treatment initiation time based on restricted mean residual lifetime

Chen, X., Song, R., Zhang, J., Adams, S. A., Sun, L., & Lu, W. (2021, July 15). Biometrics, Vol. 8.

Online Testing of Subgroup Treatment Effects Based on Value Difference

Yu, M., Lu, W., & Song, R. (2021, December 1). 2021 IEEE International Conference on Data Mining (ICDM), pp. 1463–1468.

Statistical Inference of the Value Function for Reinforcement Learning in Infinite-Horizon Settings

Shi, C., Zhang, S., Lu, W., & Song, R. (2021, December 22). Journal of the Royal Statistical Society Series B (Statistical Methodology), Vol. 12.

Testing error heterogeneity in censored linear regression

Fan, C., Lu, W., & Zhou, Y. (2021, March 31). Computational Statistics & Data Analysis, Vol. 161.

Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis

Brucker, A., Lu, W., West, R. M., Yu, Q.-Y., Hsiao, C. K., Hsiao, T.-H., … Tzeng, J.-Y. (2020, May 4). (J. Ma, Ed.). PLoS Computational Biology, Vol. 16, p. e1007797.

Model Selection With Mixed Variables on the Lasso Path

Jeng, X. J., Peng, H., & Lu, W. (2020, May 4). Sankhya B, Vol. 83, pp. 170–184.

Semiparametric estimation for proportional hazards mixture cure model allowing non-curable competing risk

Wang, Y., Zhang, J., Cai, C., Lu, W., & Tang, Y. (2020, June 27). Journal of Statistical Planning and Inference, Vol. 211, pp. 171–189.

Semiparametric estimation of the cure fraction in population‐based cancer survival analysis

Gu, E., Zhang, J., Lu, W., Wang, L., & Felizzi, F. (2020, July 28). Statistics in Medicine, Vol. 39, pp. 3787–3805.

Semiparametric regression of the illness-death model with interval censored disease incidence time: An application to the ACLS data

Zhou, J., Zhang, J., McLain, A. C., Lu, W., Sui, X., & Hardin, J. W. (2020, July 8). Statistical Methods in Medical Research, Vol. 29, pp. 3707–3720.

Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation

Shi, C., Song, R., Lu, W., & Li, R. (2020, January 4). Journal of the American Statistical Association, Vol. 116, pp. 1307–1318.

Statistical Inference for Online Decision Making: In a Contextual Bandit Setting

Chen, H., Lu, W., & Song, R. (2020, May 28). Journal of the American Statistical Association, Vol. 7, pp. 1–16.

A Sparse Random Projection-Based Test for Overall Qualitative Treatment Effects

Shi, C., Lu, W., & Song, R. (2019, April 19). Journal of the American Statistical Association, Vol. 115, pp. 1201–1213.

A Varying-Coefficient Generalized Odds Rate Model With Time-Varying Exposure: An Application to Fitness and Cardiovascular Disease Mortality

Zhou, J., Zhang, J., Mclain, A. C., Lu, W., Sui, X., & Hardin, J. W. (2019, May 27). Biometrics, Vol. 75, pp. 853–863.

Identifying individual risk rare variants using protein structure guided local tests (POINT)

West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M. A., Chang, S.-M., Wu, M. C., … Tzeng, J.-Y. (2019, February 19). PLoS Computational Biology, Vol. 15.

MODELING AND ESTIMATION OF CONTAGION-BASED SOCIAL NETWORK DEPENDENCE WITH TIME-TO-EVENT DATA

Yu, L., Lu, W., & Huang, D. (2019, January 3). Statistica Sinica, Vol. 30, pp. 2051–2074.

Modelling and estimation for optimal treatment decision with interference

Stat, 8(1).

On restricted optimal treatment regime estimation for competing risks data

Zhou, J., Zhang, J., Lu, W., & Li, X. (2019, June 25). Biostatistics, Vol. 22, pp. 217–232.

On testing conditional qualitative treatment effects

Shi, C., Song, R., & Lu, W. (2019, May 21). The Annals of Statistics, Vol. 47, pp. 2348–2377.

Robust regression for optimal individualized treatment rules

Statistics in Medicine, 38(11), 2059–2073.

SA30VARIANCE COMPONENT TEST FOR CROSS-DISORDER PATHWAY ANALYSIS

Szatkiewicz, J., Marceau, R., Yilmaz, Z., Bulik, C., Crowley, J., Mattheisen, M., … al. (2019, January 1). European Neuropsychopharmacology, Vol. 29, pp. 1204–1205.

Testing and Estimation of Social Network Dependence With Time to Event Data

Su, L., Lu, W., Song, R., & Huang, D. (2019, May 15). Journal of the American Statistical Association, Vol. 115, pp. 570–582.

Deep advantage learning for optimal dynamic treatment regime

Statistical Theory and Related Fields, 2(1), 80–88.

Entropy Learning for Dynamic Treatment Regimes

Jiang, B., Song, R., Li, J., Zeng, D., Lu, W., He, X., … Kallus, N. (2018, October 22). Statistica Sinica, Vol. 29, pp. 1633–1710.

High-dimensional $A$-learning for optimal dynamic treatment regimes

Shi, C., Fan, A., Song, R., & Lu, W. (2018, May 3). The Annals of Statistics, Vol. 46, pp. 925–957.

High-dimensional inference for personalized treatment decision

Jeng, X. J., Lu, W., & Peng, H. (2018, January 1). Electronic Journal of Statistics, Vol. 12, pp. 2074–2089.

MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL

Hu, L., Lu, W., Zhou, J., & Zhou, H. (2018, January 4). Statistica Sinica, Vol. 29, pp. 1585–1605.

Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects

Shi, C., Song, R., Lu, W., & Fu, B. (2018, May 10). Journal of the Royal Statistical Society Series B (Statistical Methodology), Vol. 80, pp. 681–702.

Sparse concordance-assisted learning for optimal treatment decision

Journal of Machine Learning Research, 18.

A Massive Data Framework for M-Estimators with Cubic-Rate

Journal of the American Statistical Association, 113(524), 1698–1709.

An improved survival estimator for censored medical costs with a kernel approach

Chen, S., Lu, W., & Zhao, H. (2017, November 27). Communication in Statistics- Theory and Methods, Vol. 47, pp. 5702–5716.

Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model With Interval-Censored Data

Zhou, J., Zhang, J., & Lu, W. (2017, September 18). Journal of Computational and Graphical Statistics, Vol. 27, pp. 48–58.

Doubly robust estimation of optimal treatment regimes for survival data—with application to an HIV/AIDS study

Jiang, R., Lu, W., Song, R., Hudgens, M. G., & Naprvavnik, S. (2017, September 1). The Annals of Applied Statistics, Vol. 11, pp. 1763–1786.

On estimation of the optimal treatment regime with the additive hazards model

Lu, W., Kang, S., & Zhang, J. (2017, April 18). Statistica Sinica, Vol. 28, pp. 1539–1560.

Semiparametric single-index model for estimating optimal individualized treatment strategy

Song, R., Luo, S., Zeng, D., Zhang, H. H., Lu, W., & Li, Z. (2017, January 1). Electronic Journal of Statistics, Vol. 11, pp. 364–384.

Subgroup detection and sample size calculation with proportional hazards regression for survival data

Statistics in Medicine, 36(29), 4646–4659.

An Expectation Maximization algorithm for fitting the generalized odds‐rate model to interval censored data

Zhou, J., Zhang, J., & Lu, W. (2016, December 21). Statistics in Medicine, Vol. 36, pp. 1157–1171.

Change-Plane Analysis for Subgroup Detection and Sample Size Calculation

Journal of the American Statistical Association, 112(518), 769–778.

Concordance-assisted learning for estimating optimal individualized treatment regimes

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(5), 1565–1582.

Efficient Estimation for Accelerated Failure Time Model under Case-Cohort and Nested Case-Control Sampling

Kang, S., Lu, W., & Liu, M. (2016, August 1). Biometrics, Vol. 73, pp. 114–123.

On estimation of optimal treatment regimes for maximizing t -year survival probability

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(4), 1165–1185.

Optimal treatment regimes for survival endpoints using locally-efficient doubly-robust estimator from a classification perspective

Lifetime Data Analysis, 23(4), 585–604.

Oracle estimation of parametric transformation models

Goldberg, Y., Lu, W., & Fine, J. (2016, January 1). Electronic Journal of Statistics, Vol. 10, pp. 90–120.

Rare variants association analysis in large-scale sequencing studies at the single locus level

PLoS Computational Biology, 12(6).

Robust learning for optimal treatment decision with NP-dimensionality

Shi, C., Song, R., & Lu, W. (2016, January 1). Electronic Journal of Statistics, Vol. 10, pp. 2894–2921.

Sequential advantage selection for optimal treatment regime

Fan, A., Lu, W., & Song, R. (2016, March 1). The Annals of Applied Statistics, Vol. 10, pp. 32–53.

A Fast Multiple‐Kernel Method With Applications to Detect Gene‐Environment Interaction

Marceau, R., Lu, W., Holloway, S., Sale, M. M., Worrall, B. B., Williams, S. R., … Tzeng, J. Y. (2015, July 3). Genetic Epidemiology, Vol. 39, pp. 456–468.

Authors' Reply to comments on ‘Sample size calculation for the proportional hazards cure model’

Authors' Reply to comments on ‘Sample size calculation for the proportional hazards cure model.’ Statistics in Medicine, 34(17), 2578–2580.

Identification of Homogeneous and Heterogeneous Variables in Pooled Cohort Studies

Cheng, X., Lu, W., & Liu, M. (2015, March 2). Biometrics, Vol. 71, pp. 397–403.

local buckley-james estimation for heteroscedastic accelerated failure time model

Statistica Sinica, 25(3), 863–877.

A model-free machine learning method for risk classification and survival probability prediction

Stat, 3(1), 337–350.

Accelerated intensity frailty model for recurrent events data

Liu, B., Lu, W., & Zhang, J. (2014, March 3). Biometrics, Vol. 70, pp. 579–587.

Censored rank independence screening for high-dimensional survival data

Biometrika, 101(4), 799–814.

Forward Stagewise Shrinkage and Addition for High Dimensional Censored Regression

Guo, Z., Lu, W., & Li, L. (2014, April 29). Statistics in Biosciences, Vol. 7, pp. 225–244.

Gene-level pharmacogenetic analysis on survival outcomes using gene-trait similarity regression

Tzeng, J.-Y., Lu, W., & Hsu, F.-C. (2014, June 1). The Annals of Applied Statistics, Vol. 8, pp. 1232–1255.

Groupwise Dimension Reduction via Envelope Method

Journal of the American Statistical Association, 110(512), 1515–1527.

On optimal treatment regimes selection for mean survival time

Geng, Y., Zhang, H. H., & Lu, W. (2014, December 16). Statistics in Medicine, Vol. 34, pp. 1169–1184.

Sample size calculation for the proportional hazards model with a time-dependent covariate

Wang, S., Zhang, J., & Lu, W. (2014, February 2). Computational Statistics & Data Analysis, Vol. 74, pp. 217–227.

Testing goodness‐of‐fit for the proportional hazards model based on nested case–control data

Lu, W., Liu, M., & Chen, Y. H. (2014, October 8). Biometrics, Vol. 70, pp. 845–851.

Estimation and selection of complex covariate effects in pooled nested case-control studies with heterogeneity

Liu, M., Lu, W., Krogh, V., Hallmans, G., Clendenen, T. V., & Zeleniuch-Jacquotte, A. (2013, April 30). Biostatistics, Vol. 14, pp. 682–694.

Kernel smoothed profile likelihood estimation in the accelerated failure time frailty model for clustered survival data

Liu, B., Lu, W., & Zhang, J. (2013, May 14). Biometrika, Vol. 100, pp. 741–755.

More efficient estimators for case-cohort studies

Kim, S., Cai, J., & Lu, W. (2013, June 10). Biometrika, Vol. 100, pp. 695–708.

NPHMC: An R-package for estimating sample size of proportional hazards mixture cure model

Cai, C., Wang, S., Lu, W., & Zhang, J. (2013, October 11). Computer Methods and Programs in Biomedicine, Vol. 113, pp. 290–300.