Le Bao
Bao, L., Niu, X., Imai-Eaton, J. & Brown, T. (2024). Dynamic Models Augmented by Hierarchical Data: An Application of Estimating HIV Epidemics at Sub-National Level. Biostatistics. https://doi.org/10.1093/biostatistics/kxae003
Laga, I., Niu, X., & Bao, L. (2021) Modeling the Marked Presence-only Data: A Case Study of Estimating the Female Sex Worker Size in Malawi. Journal of the American Statistical Association. 117.537, 27-37; https://doi.org/10.1080/01621459.2021.1944873
Fraley, C., & Raftery, A.E. (2002). Model-Based Clustering, Discriminant Analysis, and Density Estimation. Journal of the American Statistical Association, 97(458), 611–631. https://doi.org/10.1198/016214502760047131
Gelman, A., Hwang, J. & Vehtari, A. Understanding predictive information criteria for Bayesian models. Stat Comput 24, 997–1016 (2014). https://doi.org/10.1007/s11222-013-9416-2
Sam Baugh
Baugh, S., & McKinnon, K. (2022). Bayesian Quantification of Covariance Matrix Estimation Uncertainty in Optimal Fingerprinting. arXiv preprint arXiv:2208.02919.
Baugh, S., & McKinnon, K. (2022). Hierarchical Bayesian modeling of ocean heat content and its uncertainty. The Annals of Applied Statistics, 16(4), 2603-2625.
Hannart, A., Pearl, J., Otto, F. E. L., Naveau, P., & Ghil, M. (2016). Causal counterfactual theory for the attribution of weather and climate-related events. Bulletin of the American Meteorological Society, 97(1), 99-110.
Paciorek, Christopher J., and Mark J. Schervish. “Spatial modelling using a new class of nonstationary covariance functions.” Environmetrics: The official journal of the International Environmetrics Society 17.5 (2006): 483-506.
Matt Beckman
Beckman, M. D., & delMas, R. C. (2018). Statistics students’ identification of inferential model elements within contexts of their own invention. ZDM Mathematics Education 50 (7). DOI: 10.1007/s11858-018-0986-5
Lloyd, S. E., Beckman, M., Pearl, D., Passonneau, R., Li, Z., & Wang, Z. (2022). Foundations for NLP-Assisted Formative Assessment Feedback for Short-Answer Tasks in Large-Enrollment Classes. In S. A. Peters, L. Zapata-Cardona, F. Bonafini, & A. Fan (Eds.), Bridging the Gap: Empowering & Educating Today’s Learners in Statistics. Proceedings of the 11th International Conference on Teaching Statistics (ICOTS11), Rosario, Argentina. ISI/IASE.
Wild, C. J., & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. International statistical review, 67(3), 223-248.
Fleischer, Y., Biehler, R., & Schulte, C. (2022). Teaching and learning data-driven machine learning with educationally Designed Jupyter Notebooks. Statistics Education Research Journal, 21(2), 7-7.
Stephen Berg
Berg, S., & Song, H. (2023). Efficient shape-constrained inference for the autocovariance sequence from a reversible Markov chain. The Annals of Statistics, 51(6), 2440-2470.
Berg, S., Zhu, J., & Clayton, M. K. (2019). Control variates and Rao-Blackwellization for deterministic sweep Markov chains. arXiv preprint arXiv:1912.06926.
Tierney, L. (1994). Markov chains for exploring posterior distributions. the Annals of Statistics, 1701-1728.
Bottou, L. (1998). Online learning and stochastic approximations. Online learning in neural networks, 17(9), 142.
Murali Haran
Kang, B., Hughes, J., & Haran, M. (2021). Diagnostics for Monte Carlo Algorithms for Models with Intractable Normalizing Functions. arXiv preprint arXiv:2109.05121.
Lee, B. S., & Haran, M. (2022). PICAR: An efficient extendable approach for fitting hierarchical spatial models. Technometrics, 64(2), 187-198.
Robbins, H., & Monro, S. (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics, 22(3), 400–407. http://www.jstor.org/stable/2236626
Hastings, W. K. (1970). Monte Carlo Sampling Methods Using Markov Chains and Their Applications. Biometrika, 57(1), 97–109. https://doi.org/10.2307/2334940
Dave Hunter
Vu, D. Q., Hunter, D. R., and Schweinberger, M. (2013), Model-Based Clustering of Large Networks, Annals of Applied Statistics, 7 (2): 1010-1039.
Hunter, D. R., Kuruppumullage Don, P., and Lindsay, B. G. (2019). An Expansive View of EM Algorithms. In Handbook of Mixture Analysis, S. Fruehwirth-Schnatter, G. Celeux, and C. P. Robert, editors.
Newman, M. E. J. (2023). Efficient computation of rankings from pairwise comparisons. Journal of Machine Learning Research 24: 238.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society: series B (methodological), 39(1), 1-22.
Prabhani Kuruppumullage Don
Bartolucci, F., Chiaromonte, F., Don, P. K., & Lindsay, B. G. (2017). Composite likelihood inference in a discrete latent variable model for two-way “clustering-by-segmentation” problems. Journal of Computational and Graphical Statistics, 26(2), 388-402.
Varin, C., Reid, N., & Firth, D. (2011). An overview of composite likelihood methods. Statistica Sinica, 5-42.
Fraley, C., & Raftery, A. E. (1998). How many clusters? Which clustering method? Answers via model-based cluster analysis. The computer journal, 41(8), 578-588.
Nicole Lazar
Lazar, N.A. (2003) Bayesian empirical likelihood. Biometrika, 90(2), 319-326.
Wasserstein, R.L., Schirm, A.L., and Lazar, N.A. (2019) Moving to a world beyond “p<0.05”. The American Statistician, 73 (Sup 1), 1-19.
Efron, B. (1981) Nonparametric standard errors and confidence intervals. Canadian Journal of Statistics, 9(2), 139-172.
Kass, R.E., Caffo, B.S., Davidian, M., Meng, X.-L., Yu, B., and Reid, N. (2016) Ten simple rules for effective statistical practice. PLOS Computational Biology, 12(6), e1004961.
Jia Li
Beomseok Seo, Lin Lin, Jia Li, “Mixture of linear models co-supervised by deep neural networks,” Journal of Computational and Graphical Statistics, 31(4):1303-1317, 2022.
Yukun Chen, Jianbo Ye, Jia Li, “Aggregated Wasserstein Distance and State Registration for Hidden Markov Models,” IEEE Transaction on Pattern Analysis and Machine Intelligence, 42(9):2133-2147, 2020.
Qunhua Li
Li, Q., Brown, J. B., Huang, H., & Bickel, P. J. (2011). Measuring reproducibility of high-throughput experiments.
Koch, H., Keller, C. A., Xiang, G., Giardine, B., Zhang, F., Wang, Y., … & Li, Q. (2022). CLIMB: High-dimensional association detection in large scale genomic data. Nature communications, 13(1), 6874.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological), 57(1), 289-300.
Wang, G., Sarkar, A., Carbonetto, P., & Stephens, M. (2020). A simple new approach to variable selection in regression, with application to genetic fine mapping. Journal of the Royal Statistical Society Series B: Statistical Methodology, 82(5), 1273-1300.
Runze Li
Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and it oracle properties, Journal of American Statistical Association. 96, 1348-1360.
Li, R. and Liang, H. (2008). Variable selection in semiparametric regression modeling. Annals of Statistics. 36, 261-286.
Zou, H. (2006). Adaptive Lasso and its oracle properties, Journal of American Statistical Association. 101, 1418-1429.
Ning, Y. and Liu, H. (2017). A general theory of hypothesis tests and confidence regions for sparse high dimensional models. Annals of Statistics, 45, 158 – 195.
Michael Schweinberger
Schweinberger, M. and Handcock, M. S. (2015). Local dependence in random graph models: Characterization, properties and statistical inference. Journal of the Royal Statistical Society, Series B, 77 647–676. https://doi.org/10.1111/rssb.12081
Stewart, J.R and Schweinberger, M. (2023). Pseudo-likelihood-based M-estimation of random graphs with dependent edges and parameter vectors of increasing dimension. https://arxiv.org/abs/2012.07167
Ghosal, S. and van der Vaart, A. (2007). Convergence rates of posterior distributions for noniid observations. The Annals of Statistics, 35, 192-223.
Li, S. and Wager, S. (2022). Random graph asymptotics for treatment effect estimation under network interference. The Annals of Statistics, 50, 2334-2358.
Hyebin Song
Berg, S., & Song, H. (2023). Efficient shape-constrained inference for the autocovariance sequence from a reversible Markov chain. The Annals of Statistics, 51(6), 2440-2470.
Song, H., Bremer, B. J., Hinds, E. C., Raskutti, G., & Romero, P. A. (2021). Inferring protein sequence-function relationships with large-scale positive-unlabeled learning. Cell systems, 12(1), 92-101.
Guntuboyina, A., & Sen, B. (2018). Nonparametric shape-restricted regression. Statistical Science, 33(4), 568-594.
Negahban, S. N., Ravikumar, P., Wainwright, M. J., & Yu, B. (2012). A unified framework for high-dimensional analysis of M-estimators with decomposable regularizers.
Bharath Sriperumbudur
Gromov-Wasserstein distances: Entropic regularization, duality, and sample complexity Z. Zhang, Z. Goldfeld, Y. Mroueh, and B. K. Sriperumbudur
https://arxiv.org/pdf/2212.12848.pdf
Kernel mean embedding of distributions: A review and beyond K. Muandet, K. Fukumizu, B. K. Sriperumbudur and B. Scholkopf Foundations and Trends in Machine Learning, 10(1-2):1-141, 2017.
https://arxiv.org/pdf/1605.09522
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Q. Liu, and D. Wang, NIPS 2016
https://proceedings.neurips.cc/paper/2016/file/b3ba8f1bee1238a2f37603d90b58898d-Paper.pdf
A kernel statistical test of independence A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Scholkopf and A. J. Smola, NIPS 2007
https://proceedings.neurips.cc/paper_files/paper/2007/file/d5cfead94f5350c12c322b5b664544c1-Paper.pdf
Hyungsuk Tak
Meyer, Antoine D., et al. (2023). TD-Carma: Painless, accurate, and scalable estimates of gravitational lens time delays with flexible Carma Processes. vol. 950, no. 1, 2023, p. 37. https://doi.org/10.3847/1538-4357/acbea1.
Hu, Zhirui, and Hyungsuk Tak (2020). Modeling Stochastic Variability in Multiband Time-series Data. The Astronomical Journal vol. 160, no. 6, p. 265. https://doi.org/10.3847/1538-3881/abc1e2
Kelly, Brandon C., et al. (2014). Flexible and scalable methods for quantifying stochastic variability in the era of massive time-domain astronomical data sets. The Astrophysical Journal, vol. 788, no. 1, p. 33, https://doi.org/10.1088/0004-637x/788/1/33.
Kelly, Brandon C., Jill Bechtold, et al. (2009). Are the variations in quasar optical flux driven by thermal fluctuations? The Astrophysical Journal, vol. 698, no. 1, pp. 895–910, https://doi.org/10.1088/0004-637x/698/1/895.
Sudha Veturi
Veturi, Y., de Los Campos, G., Yi, N., Huang, W., Vazquez, A. I., & Kühnel, B. (2019). Modeling heterogeneity in the genetic architecture of ethnically diverse groups using random effect interaction models. Genetics, 211(4), 1395-1407.
Vazquez, A. I., Veturi, Y., Behring, M., Shrestha, S., Kirst, M., Resende Jr, M. F., & de Los Campos, G. (2016). Increased proportion of variance explained and prediction accuracy of survival of breast cancer patients with use of whole-genome multiomic profiles. Genetics, 203(3), 1425-1438.
Gianola, D., de Los Campos, G., Hill, W. G., Manfredi, E., & Fernando, R. (2009). Additive genetic variability and the Bayesian alphabet. Genetics, 183(1), 347-363.
Zhou, W., Nielsen, J. B., Fritsche, L. G., Dey, R., Gabrielsen, M. E., Wolford, B. N., … & Lee, S. (2018). Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nature genetics, 50(9), 1335-1341.
Lingzhou Xue
Zheng, Z., Gao, F., Xue, L. and Yang, J. (2024) Federated Q-Learning: Linear Regret Speedup with
Low Communication Cost. Proceedings of The Twelfth International Conference on Learning Representations (ICLR), in press. Available at https://arxiv.org/abs/2312.15023
Zhang, Q., Xue, L., and Bing Li. (2023+) Dimension Reduction for Fréchet Regression. Journal of the American Statistical Association, in press. https://doi.org/10.1080/01621459.2023.2277406
Yu, X., Li, D. and Xue, L. (2022+) Fisher’s Combined Probability Test for High-Dimensional Covariance Matrices. Journal of the American Statistical Association, in press. https://doi.org/10.1080/01621459.2022.2126781
Srinivasan, A., Xue, L. and Zhan, X. (2021) Compositional Knockoff Filter for FDR Control in Microbiome Regression Analysis. Biometrics. 77: 984–995. https://doi.org/10.1111/biom.13336
Yubai Yuan
Yubai Yuan & Annie Qu (2023) De-confounding Causal Inference Using Latent Multiple-Mediator Pathways, Journal of the American Statistical Association, DOI: 10.1080/01621459.2023.2240461
Yuan, Y., & Qu, A. (2021). Community detection with dependent connectivity. The Annals of Statistics, 49(4), 2378-2428.
Rodriguez, M. G., Leskovec, J., Balduzzi, D., & Schölkopf, B. (2014). Uncovering the structure and temporal dynamics of information propagation. Network Science, 2(1), 26-65.
Farajtabar, M., Wang, Y., Gomez-Rodriguez, M., Li, S., Zha, H., & Song, L. (2017). Coevolve: A joint point process model for information diffusion and network evolution. Journal of Machine Learning Research, 18(41), 1-49.
Zhibiao Zhao
Xu, Z., Kim, S. and Zhao, Z. (2022) Locally stationary quantile regression for inflation and interest rates. Journal of Business and Economic Statistics, 40 838-851.
Zhao, Z. (2015) Inference for local autocorrelation process in locally stationary models. Journal of Business and Economic Statistics, 33 296-306.
Vogt, M. (2012) Nonparametric regression for locally stationary time series. Annals of Statistics 40 2601-2633.
Dahlhaus, R. (2012) Locally Stationary Processes. in Handbook of Statistics, Time Series Analysis: Methods and Applications (Vol. 30), eds. T. Subba Rao, S. Subba Rao, and C. R. Rao, Amsterdam: North Holland, pp. 351–413.