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Wang, F., Zhang, W., & Yao, F. (2025). Computationally efficient whole-genome signal region detection for quantitative and binary traits. The Annals of Applied Statistics, 19(2). https://doi.org/10.1214/25-aoas2029

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Xie, W., Zeng, D., & Wang, Y. (2024). Support vector machine for dynamic survival prediction with time-dependent covariates. The Annals of Applied Statistics, 18(3). https://doi.org/10.1214/24-aoas1875

Mi, X., Bekerman, W., Rustgi, A. K., Sims, P. A., Canoll, P. D., & Hu, J. (2024). RZiMM-scRNA: A regularized zero-inflated mixture model framework for single-cell RNA-seq data. The Annals of Applied Statistics, 18(1). https://doi.org/10.1214/23-aoas1761

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Anthopolos, R., Chen, Q., Sedransk, J., Thompson, M., Meng, G., & Galea, S. (2023). A Bayesian growth mixture model for complex survey data: Clustering postdisaster PTSD trajectories. The Annals of Applied Statistics, 17(3). https://doi.org/10.1214/23-aoas1729

Yao, Y., Ogden, R. T., Zeng, C., & Chen, Q. (2023). Bivariate hierarchical Bayesian model for combining summary measures and their uncertainties from multiple sources. The Annals of Applied Statistics, 17(2). https://doi.org/10.1214/22-aoas1699

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Cheung, Y. K., Chandereng, T., & Diaz, K. M. (2022). A novel framework to estimate multidimensional minimum effective doses using asymmetric posterior gain and ϵ-tapering. The Annals of Applied Statistics, 16(3). https://doi.org/10.1214/21-aoas1549

Wang, T., Ionita-Laza, I., & Wei, Y. (2022). Integrated Quantile RAnk Test (iQRAT) for gene-level associations. The Annals of Applied Statistics, 16(3). https://doi.org/10.1214/21-aoas1548

Hu, L., Zou, J., Gu, C., Ji, J., Lopez, M., & Kale, M. (2022). A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data. The Annals of Applied Statistics, 16(2). https://doi.org/10.1214/21-aoas1530

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