Wenbin Lu

Rhodes, G., Davidian, M., & Lu, W. (2024, February 9). Estimation of optimal treatment regimes with electronic medical record data using the residual life value estimator. BIOSTATISTICS, Vol. 2. https://doi.org/10.1093/biostatistics/kxae002

Wang, R., Cen, M., Huang, Y., Qian, G., Dean, N. E., Ellenberg, S. S., … Longini, I. M. (2024, February 13). Methods for the estimation of direct and indirect vaccination effects by combining data from individual- and cluster-randomized trials. STATISTICS IN MEDICINE. https://doi.org/10.1002/sim.10030

Rhodes, G., Davidian, M., & Lu, W. (2023). DYNAMIC PREDICTION OF RESIDUAL LIFE WITH LONGITUDINAL COVARIATES USING LONG SHORT-TERM MEMORY NETWORKS. ANNALS OF APPLIED STATISTICS, 17(3), 2039–2058. https://doi.org/10.1214/22-AOAS1706

Chen, H., Lu, W., Song, R., & Ghosh, P. (2023, April 12). On Learning and Testing of Counterfactual Fairness through Data Preprocessing. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol. 4. https://doi.org/10.1080/01621459.2023.2186885

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

Liu, Y., Song, R., Lu, W., & Xiao, Y. (2022, March 10). A Probit Tensor Factorization Model For Relational Learning. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 3. https://doi.org/10.1080/10618600.2021.2003204

Johnson, D., Lu, W., & Davidian, M. (2022, August 2). A general framework for subgroup detection via one-step value difference estimation. BIOMETRICS, Vol. 8. https://doi.org/10.1111/biom.13711

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

Cai, H., Lu, W., West, R. M., Mehrotra, D. V., & Huang, L. (2022, July 7). CAPITAL: Optimal subgroup identification via constrained policy tree search. STATISTICS IN MEDICINE. https://doi.org/10.1002/sim.9507

Jin, P., Lu, W., Chen, Y., & Liu, M. (2022, October 24). Change-plane analysis for subgroup detection with a continuous treatment. BIOMETRICS. https://doi.org/10.1111/biom.13762

Li, D., Lu, W., Shu, D., Toh, S., & Wang, R. (2022, February 23). Distributed Cox proportional hazards regression using summary-level information. BIOSTATISTICS. https://doi.org/10.1093/biostatistics/kxac006

Zhao, G., Ma, Y., & Lu, W. (2022). EFFICIENT ESTIMATION FOR DIMENSION REDUCTION WITH CENSORED SURVIVAL DATA. STATISTICA SINICA, 32, 2359–2380. https://doi.org/10.5705/ss.202020.0404

Weaver, C., Xiao, L., & Lu, W. (2022, March 28). Functional data analysis for longitudinal data with informative observation times. BIOMETRICS, Vol. 3. https://doi.org/10.1111/biom.13646

Cook, K., Lu, W., & Wang, R. (2022, December 1). Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates. BIOMETRICS. https://doi.org/10.1111/biom.13787

Huang, C., Callahan, B. J., Wu, M. C., Holloway, S. T., Brochu, H., Lu, W., … Tzeng, J.-Y. (2022). Phylogeny-guided microbiome OTU-specific association test (POST). MICROBIOME, 10(1). https://doi.org/10.1186/s40168-022-01266-3

Zhou, J., Zhang, J., & Lu, W. (2022). TransModel: An R Package for Linear Transformation Model with Censored Data. JOURNAL OF STATISTICAL SOFTWARE, 101(9). https://doi.org/10.18637/jss.v101.i09

Shi, C., Song, R., & Lu, W. (2021). Concordance and Value Information Criteria for Optimal Treatment Decision. Annals of Statistics, 49(1), 49–75. https://doi.org/10.1214/19-AOS1908

Cai, H., Song, R., & Lu, W. (2021). GEAR: On optimal decision making with auxiliary data. STAT, 10(1). https://doi.org/10.1002/sta4.399

Chang, S.-M., Yang, M., Lu, W., Huang, Y.-J., Huang, Y., Hung, H., … Tzeng, J.-Y. (2021). Gene-set integrative analysis of multi-omics data using tensor-based association test. BIOINFORMATICS, 37(16), 2259–2265. https://doi.org/10.1093/bioinformatics/btab125

Jeng, X. J., Peng, H., & Lu, W. (2021). Model Selection With Mixed Variables on the Lasso Path. SANKHYA-SERIES B-APPLIED AND INTERDISCIPLINARY STATISTICS, 83(1), 170–184. https://doi.org/10.1007/s13571-019-00219-5

Chen, X., Song, R., Zhang, J., Adams, S. A., Sun, L., & Lu, W. (2021, August 7). On estimating optimal regime for treatment initiation time based on restricted mean residual lifetime. BIOMETRICS, Vol. 8. https://doi.org/10.1111/biom.13530

Zhou, J., Zhang, J., Lu, W., & Li, X. (2021). On restricted optimal treatment regime estimation for competing risks data. BIOSTATISTICS, 22(2), 217–232. https://doi.org/10.1093/biostatistics/kxz026

Yu, M., Lu, W., & Song, R. (2021). Online Testing of Subgroup Treatment Effects Based on Value Difference. 2021 21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2021), pp. 1463–1468. https://doi.org/10.1109/ICDM51629.2021.00189

Wang, Y., Zhang, J., Cai, C., Lu, W., & Tang, Y. (2021). Semiparametric estimation for proportional hazards mixture cure model allowing non-curable competing risk. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 211, 171–189. https://doi.org/10.1016/j.jspi.2020.06.009

Shi, C., Song, R., Lu, W., & Li, R. (2021). Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 116(535), 1307–1318. https://doi.org/10.1080/01621459.2019.1710154

Chen, H., Lu, W., & Song, R. (2021). Statistical Inference for Online Decision Making: In a Contextual Bandit Setting. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 116(533), 240–255. https://doi.org/10.1080/01621459.2020.1770098

Shi, C., Zhang, S., Lu, W., & Song, R. (2021, December 22). Statistical inference of the value function for reinforcement learning in infinite-horizon settings. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, Vol. 12. https://doi.org/10.1111/rssb.12465

Fan, C., Lu, W., & Zhou, Y. (2021). Testing error heterogeneity in censored linear regression. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 161. https://doi.org/10.1016/j.csda.2021.107207

Shi, C., Lu, W., & Song, R. (2020). A Sparse Random Projection-Based Test for Overall Qualitative Treatment Effects. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(531), 1201–1213. https://doi.org/10.1080/01621459.2019.1604368

Brucker, A., Lu, W., West, R. M., Yu, Q.-Y., Hsiao, C. K., Hsiao, T.-H., … Tzeng, J.-Y. (2020). Association test using Copy Number Profile Curves (CONCUR) enhances power in rare copy number variant analysis. PLOS COMPUTATIONAL BIOLOGY, 16(5). https://doi.org/10.1371/journal.pcbi.1007797

Yu, L., Lu, W., & Huang, D. (2020). MODELING AND ESTIMATION OF CONTAGION-BASED SOCIAL NETWORK DEPENDENCE WITH TIME-TO-EVENT DATA. STATISTICA SINICA, 30(4), 2051–2074. https://doi.org/10.5705/ss.202018.0222

Gu, E., Zhang, J., Lu, W., Wang, L., & Felizzi, F. (2020). Semiparametric estimation of the cure fraction in population-based cancer survival analysis. STATISTICS IN MEDICINE, 39(26), 3787–3805. https://doi.org/10.1002/sim.8693

Zhou, J., Zhang, J., McLain, A. C., Lu, W., Sui, X., & Hardin, J. W. (2020). Semiparametric regression of the illness-death model with interval censored disease incidence time: An application to the ACLS data. STATISTICAL METHODS IN MEDICAL RESEARCH, 29(12), 3707–3720. https://doi.org/10.1177/0962280220939123

Su, L., Lu, W., Song, R., & Huang, D. (2020). Testing and Estimation of Social Network Dependence With Time to Event Data. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 115(530), 570–582. https://doi.org/10.1080/01621459.2019.1617153

Zhou, J., Zhang, J., Mclain, A. C., Lu, W., Sui, X., & Hardin, J. W. (2019). A varying-coefficient generalized odds rate model with time-varying exposure: An application to fitness and cardiovascular disease mortality. BIOMETRICS, 75(3), 853–863. https://doi.org/10.1111/biom.13057

Jiang, B., Song, R., Li, J., Zeng, D., Lu, W., He, X., … Kallus, N. (2019, October). ENTROPY LEARNING FOR DYNAMIC TREATMENT REGIMES. STATISTICA SINICA, Vol. 29, pp. 1633–1710. https://doi.org/10.5705/ss.202018.0076

West, R. M., Lu, W., Rotroff, D. M., Kuenemann, M. A., Chang, S.-M., Wu, M. C., … Tzeng, J.-Y. (2019). Identifying individual risk rare variants using protein structure guided local tests (POINT). PLOS COMPUTATIONAL BIOLOGY, 15(2). https://doi.org/10.1371/journal.pcbi.1006722

Hu, L., Lu, W., Zhou, J., & Zhou, H. (2019). MM ALGORITHMS FOR VARIANCE COMPONENT ESTIMATION AND SELECTION IN LOGISTIC LINEAR MIXED MODEL. STATISTICA SINICA, 29(3), 1585–1605. https://doi.org/10.5705/ss.202017.0220

Su, L., Lu, W., & Song, R. (2019). Modelling and estimation for optimal treatment decision with interference. Stat, 8(1). https://doi.org/10.1002/STA4.219

Shi, C., Song, R., & Lu, W. (2019). ON TESTING CONDITIONAL QUALITATIVE TREATMENT EFFECTS. ANNALS OF STATISTICS, 47(4), 2348–2377. https://doi.org/10.1214/18-AOS1750

Xiao, W., Zhang, H. H., & Lu, W. (2019). Robust regression for optimal individualized treatment rules. Statistics in Medicine, 38(11), 2059–2073. https://doi.org/10.1002/SIM.8102

Szatkiewicz, J., Marceau, R., Yilmaz, Z., Bulik, C., Crowley, J., Mattheisen, M., … al. (2019). VARIANCE COMPONENT TEST FOR CROSS-DISORDER PATHWAY ANALYSIS. EUROPEAN NEUROPSYCHOPHARMACOLOGY, Vol. 29, pp. 1204–1205. https://doi.org/10.1016/j.euroneuro.2018.08.252

Shi, C., Lu, W., & Song, R. (2018). A Massive Data Framework for M-Estimators with Cubic-Rate. Journal of the American Statistical Association, 113(524), 1698–1709. https://doi.org/10.1080/01621459.2017.1360779

Chen, S., Lu, W., & Zhao, H. (2018). An improved survival estimator for censored medical costs with a kernel approach. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 47(23), 5702–5716. https://doi.org/10.1080/03610926.2017.1400059

Zhou, J., Zhang, J., & Lu, W. (2018). Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model With Interval-Censored Data. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 27(1), 48–58. https://doi.org/10.1080/10618600.2017.1349665

Liang, S., Lu, W., & Song, R. (2018). Deep advantage learning for optimal dynamic treatment regime. Statistical Theory and Related Fields, 2(1), 80–88. https://doi.org/10.1080/24754269.2018.1466096

Shi, C., Fan, A., Song, R., & Lu, W. (2018). HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES. ANNALS OF STATISTICS, 46(3), 925–957. https://doi.org/10.1214/17-aos1570

Jeng, X. J., Lu, W., & Peng, H. (2018). High-dimensional inference for personalized treatment decision. ELECTRONIC JOURNAL OF STATISTICS, 12(1), 2074–2089. https://doi.org/10.1214/18-ejs1439

Shi, C., Song, R., Lu, W., & Fu, B. (2018). Maximin projection learning for optimal treatment decision with heterogeneous individualized treatment effects. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 80(4), 681–702. https://doi.org/10.1111/rssb.12273

Kang, S., Lu, W., & Zhang, J. (2018). ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL. STATISTICA SINICA, 28(3), 1539–1560. https://doi.org/10.5705/ss.202016.0543

Liang, S. H., Lu, W. B., Song, R., & Wang, L. (2018). Sparse concordance-assisted learning for optimal treatment decision. Journal of Machine Learning Research, 18.

Zhou, J., Zhang, J., & Lu, W. (2017). An Expectation Maximization algorithm for fitting the generalized odds-rate model to interval censored data. STATISTICS IN MEDICINE, 36(7), 1157–1171. https://doi.org/10.1002/sim.7204

Fan, A., Song, R., & Lu, W. (2017). Change-Plane Analysis for Subgroup Detection and Sample Size Calculation. Journal of the American Statistical Association, 112(518), 769–778. https://doi.org/10.1080/01621459.2016.1166115

Jiang, R., Lu, W., Song, R., Hudgens, M. G., & Naprvavnik, S. (2017). DOUBLY ROBUST ESTIMATION OF OPTIMAL TREATMENT REGIMES FOR SURVIVAL DATA-WITH APPLICATION TO AN HIV/AIDS STUDY. ANNALS OF APPLIED STATISTICS, 11(3), 1763–1786. https://doi.org/10.1214/17-aoas1057

Kang, S., Lu, W., & Liu, M. (2017). Efficient Estimation for Accelerated Failure Time Model under Case-Cohort and Nested Case-Control Sampling. BIOMETRICS, 73(1), 114–123. https://doi.org/10.1111/biom.12573

Bai, X., Tsiatis, A., Lu, W., & Song, R. (2017). Optimal treatment regimes for survival endpoints using locally-efficient doubly-robust estimator from a classification perspective. Lifetime Data Analysis, 23(4), 585–604. https://doi.org/10.1007/s10985-016-9376-x

Song, R., Luo, S., Zeng, D., Zhang, H. H., Lu, W., & Li, Z. (2017). Semiparametric single-index model for estimating optimal individualized treatment strategy. ELECTRONIC JOURNAL OF STATISTICS, 11(1), 364–384. https://doi.org/10.1214/17-ejs1226

Kang, S., Lu, W., & Song, R. (2017). Subgroup detection and sample size calculation with proportional hazards regression for survival data. Statistics in Medicine, 36(29), 4646–4659. https://doi.org/10.1002/sim.7441

Fan, C., Lu, W., Song, R., & Zhou, Y. (2016). Concordance-assisted learning for estimating optimal individualized treatment regimes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(5), 1565–1582. https://doi.org/10.1111/rssb.12216

Jiang, R., Lu, W., Song, R., & Davidian, M. (2016). 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. https://doi.org/10.1111/rssb.12201

Goldberg, Y., Lu, W., & Fine, J. (2016). Oracle estimation of parametric transformation models. ELECTRONIC JOURNAL OF STATISTICS, 10(1), 90–120. https://doi.org/10.1214/15-ejs1083

Jeng, X. J., Daye, Z. J., Lu, W. B., & Tzeng, J. Y. (2016). Rare variants association analysis in large-scale sequencing studies at the single locus level. PLoS Computational Biology, 12(6).

Shi, C., Song, R., & Lu, W. (2016). Robust learning for optimal treatment decision with NP-dimensionality. ELECTRONIC JOURNAL OF STATISTICS, 10(2), 2894–2921. https://doi.org/10.1214/16-ejs1178

Fan, A., Lu, W., & Song, R. (2016). SEQUENTIAL ADVANTAGE SELECTION FOR OPTIMAL TREATMENT REGIME. ANNALS OF APPLIED STATISTICS, 10(1), 32–53. https://doi.org/10.1214/15-aoas849

Marceau, R., Lu, W., Holloway, S., Sale, M. M., Worrall, B. B., Williams, S. R., … Tzeng, J.-Y. (2015). A Fast Multiple-Kernel Method With Applications to Detect Gene-Environment Interaction. GENETIC EPIDEMIOLOGY, 39(6), 456–468. https://doi.org/10.1002/gepi.21909

Wang, S., Zhang, J., & Lu, W. (2015). Authors' Reply to comments on ‘Sample size calculation for the proportional hazards cure model.’ Statistics in Medicine, 34(17), 2578–2580. https://doi.org/10.1002/SIM.6491

Guo, Z., Lu, W., & Li, L. (2015). Forward Stagewise Shrinkage and Addition for High Dimensional Censored Regression. STATISTICS IN BIOSCIENCES, 7(2), 225–244. https://doi.org/10.1007/s12561-014-9114-4

Guo, Z., Li, L., Lu, W., & Li, B. (2015). Groupwise Dimension Reduction via Envelope Method. Journal of the American Statistical Association, 110(512), 1515–1527. https://doi.org/10.1080/01621459.2014.970687

Cheng, X., Lu, W., & Liu, M. (2015). Identification of homogeneous and heterogeneous variables in pooled cohort studies. BIOMETRICS, 71(2), 397–403. https://doi.org/10.1111/biom.12285

Geng, Y., Zhang, H. H., & Lu, W. (2015). On optimal treatment regimes selection for mean survival time. STATISTICS IN MEDICINE, 34(7), 1169–1184. https://doi.org/10.1002/sim.6397

Pang, L., Lu, W. B., & Wang, H. J. (2015). local buckley-james estimation for heteroscedastic accelerated failure time model. Statistica Sinica, 25(3), 863–877.

Geng, Y., Lu, W., & Zhang, H. H. (2014). A model-free machine learning method for risk classification and survival probability prediction. Stat, 3(1), 337–350. https://doi.org/10.1002/STA4.67

Liu, B., Lu, W., & Zhang, J. (2014). Accelerated Intensity Frailty Model for Recurrent Events Data. BIOMETRICS, 70(3), 579–587. https://doi.org/10.1111/biom.12163

Song, R., Lu, W., Ma, S., & Jeng, X. (J. (2014). Censored rank independence screening for high-dimensional survival data. Biometrika, 101(4), 799–814. https://doi.org/10.1093/biomet/asu047

Tzeng, J.-Y., Lu, W., & Hsu, F.-C. (2014). GENE-LEVEL PHARMACOGENETIC ANALYSIS ON SURVIVAL OUTCOMES USING GENE-TRAIT SIMILARITY REGRESSION. ANNALS OF APPLIED STATISTICS, 8(2), 1232–1255. https://doi.org/10.1214/14-aoas735

Cai, C., Wang, S., Lu, W., & Zhang, J. (2014). NPHMC: An R-package for estimating sample size of proportional hazards mixture cure model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 113(1), 290–300. https://doi.org/10.1016/j.cmpb.2013.10.001

Wang, S., Zhang, J., & Lu, W. (2014). Sample size calculation for the proportional hazards model with a time-dependent covariate. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 74, 217–227. https://doi.org/10.1016/j.csda.2014.01.018

Lu, W., Liu, M., & Chen, Y.-H. (2014). Testing Goodness-of-Fit for the Proportional Hazards Model based on Nested Case-Control Data. BIOMETRICS, 70(4), 845–851. https://doi.org/10.1111/biom.12239

Kim, J. P., Lu, W., Sit, T., & Ying, Z. (2013). A Unified Approach to Semiparametric Transformation Models Under General Biased Sampling Schemes. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 108(501), 217–227. https://doi.org/10.1080/01621459.2012.746073

Liu, M., Lu, W., Krogh, V., Hallmans, G., Clendenen, T. V., & Zeleniuch-Jacquotte, A. (2013). Estimation and selection of complex covariate effects in pooled nested case-control studies with heterogeneity. BIOSTATISTICS, 14(4), 682–694. https://doi.org/10.1093/biostatistics/kxt015

Liu, B., Lu, W., & Zhang, J. (2013). Kernel smoothed profile likelihood estimation in the accelerated failure time frailty model for clustered survival data. BIOMETRIKA, 100(3), 741–755. https://doi.org/10.1093/biomet/ast012

Kim, S., Cai, J., & Lu, W. (2013). More efficient estimators for case-cohort studies. BIOMETRIKA, 100(3), 695–708. https://doi.org/10.1093/biomet/ast018

Shang, S., Liu, M., Zeleniuch-Jacquotte, A., Clendenen, T. V., Krogh, V., Hallmans, G., & Lu, W. (2013). Partially linear single index Cox regression model in nested case-control studies. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 67, 199–212. https://doi.org/10.1016/j.csda.2013.05.011

Sui, X. M., Zhang, J. J., Lee, D. C., Church, T. S., Lu, W. B., Liu, J. X., & Blair, S. N. (2013). Physical activity/fitness peaks during perimenopause and BMI change patterns are not associated with baseline activity/fitness in women: a longitudinal study with a median 7-year follow-up. British Journal of Sports Medicine, 47(2), 77–82. https://doi.org/10.1136/bjsports-2011-090888

Lu, W., Zhang, H. H., & Zeng, D. (2013). Variable selection for optimal treatment decision. STATISTICAL METHODS IN MEDICAL RESEARCH, 22(5), 493–504. https://doi.org/10.1177/0962280211428383

Yan, S., Zhang, D., Lu, W., Grifo, J. A., & Liu, M. (2012). A Semi-nonparametric Approach to Joint Modeling of A Primary Binary Outcome and Longitudinal Data Measured at Discrete Informative Times. Statistics in Biosciences, 4(2), 213–234. https://doi.org/10.1007/S12561-011-9053-2

Lu, W., & Liu, M. (2012). On estimation of linear transformation models with nested case-control sampling. LIFETIME DATA ANALYSIS, 18(1), 80–93. https://doi.org/10.1007/s10985-011-9203-3

Lu, W., Goldberg, Y., & Fine, J. P. (2012). On the robustness of the adaptive lasso to model misspecification. BIOMETRIKA, 99(3), 717–731. https://doi.org/10.1093/biomet/ass027

Wang, S., Zhang, J., & Lu, W. (2012). Sample size calculation for the proportional hazards cure model. STATISTICS IN MEDICINE, 31(29), 3959–3971. https://doi.org/10.1002/sim.5465

Pang, L., Lu, W., & Wang, H. J. (2012). Variance estimation in censored quantile regression via induced smoothing. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 56(4), 785–796. https://doi.org/10.1016/j.csda.2010.10.018

Lu, W., & Li, L. (2011). Sufficient Dimension Reduction for Censored Regressions. BIOMETRICS, 67(2), 513–523. https://doi.org/10.1111/j.1541-0420.2010.01490.x

Liu, M., Lu, W., & Tseng, C.-hong. (2010). Cox Regression in Nested Case-Control Studies with Auxiliary Covariates. BIOMETRICS, 66(2), 374–381. https://doi.org/10.1111/j.1541-0420.2009.01277.x

Liu, M., Lu, W., Shore, R. E., & Zeleniuch-Jacquotte, A. (2010). Cox regression model with time-varying coefficients in nested case-control studies. BIOSTATISTICS, 11(4), 693–706. https://doi.org/10.1093/biostatistics/kxq037

Lu, W. B. (2010). Efficient estimation for an accelerated failure time model with a cure fraction. Statistica Sinica, 20(2), 661–674.

Cai, Y. X., Chow, M. Y., Lu, W. B., & Li, L. X. (2010). Evaluation of distribution fault diagnosis algorithms using ROC curves. Ieee power and energy soceity general meeting 2010. https://doi.org/10.1109/pes.2010.5588154

Tzeng, J.-Y., Lu, W., Farmen, M. W., Liu, Y., & Sullivan, P. F. (2010). Haplotype-Based Pharmacogenetic Analysis for Longitudinal Quantitative Traits in the Presence of Dropout. JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 20(2), 334–350. https://doi.org/10.1080/10543400903572787

Lu, W., & Zhang, H. H. (2010). On Estimation of Partially Linear Transformation Models. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 105(490), 683–691. https://doi.org/10.1198/jasa.2010.tm09302

Zhang, H. H., Lu, W., & Wang, H. (2010). On sparse estimation for semiparametric linear transformation models. JOURNAL OF MULTIVARIATE ANALYSIS, 101(7), 1594–1606. https://doi.org/10.1016/j.jmva.2010.01.015

Shows, J. H., Lu, W., & Zhang, H. H. (2010). Sparse estimation and inference for censored median regression. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 140(7), 1903–1917. https://doi.org/10.1016/j.jspi.2010.01.043

WenBin, L. (2009). Efficiency comparison between mean and log-rank tests for recurrent event time data. SCIENCE IN CHINA SERIES A-MATHEMATICS, 52(6), 1169–1180. https://doi.org/10.1007/s11425-009-0059-x

Liang, Y., Lu, W., & Ying, Z. (2009). Joint Modeling and Analysis of Longitudinal Data with Informative Observation Times. BIOMETRICS, 65(2), 377–384. https://doi.org/10.1111/j.1541-0420.2008.01104.x

Liu, M., Lu, W., & Shao, Y. (2008). A Monte Carlo approach for change-point detection in the Cox proportional hazards model. STATISTICS IN MEDICINE, 27(19), 3894–3909. https://doi.org/10.1002/sim.3214

Lu, W. B., & Liang, Y. (2008). Analysis of competing risks data with missing cause of failure under additive hazards model. Statistica Sinica, 18(1), 219–234.

Lu, W., & Li, L. (2008). Boosting method for nonlinear transformation models with censored survival data. BIOSTATISTICS, 9(4), 658–667. https://doi.org/10.1093/biostatistics/kxn005

Lu, W. (2008). Maximum likelihood estimation in the proportional hazards cure model. ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, 60(3), 545–574. https://doi.org/10.1007/s10463-007-0120-x

Lu, W., & Peng, L. (2008). Semiparametric analysis of mixture regression models with competing risks data. LIFETIME DATA ANALYSIS, 14(3), 231–252. https://doi.org/10.1007/s10985-007-9077-6

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