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Summer Readings – Selected Faculty Publications 2020 – 2021
Department of Statistics, Columbia University
July 2021
(Prepared by Disa Yu and Tian Zheng)
1. Avella-Medina, M. (2021). Privacy-preserving parametric inference: a case for robust statistics. Journal of the American Statistical Association, 116(534), 969-983.
https://www.tandfonline.com/doi/abs/10.1080/01621459.2019.1700130
2. Wang, Y., & Blei, D. (2021, July). A Proxy Variable View of Shared Confounding. In International Conference on Machine Learning (pp. 10697-10707). PMLR.
http://proceedings.mlr.press/v139/wang21c.html
3. Davis, R. A., Fokianos, K., Holan, S. H., Joe, H., Livsey, J., Lund, R., … & Ravishanker, N. (2021). Count time series: A methodological review. Journal of the American Statistical Association, 1-15.
https://www.tandfonline.com/doi/full/10.1080/01621459.2021.1904957
4. Chong, C. (2020). High-frequency analysis of parabolic stochastic PDEs. The Annals of Statistics, 48(2), 1143-1167. https://projecteuclid.org/journals/annals-of-statistics/volume-48/issue-2/High-frequency-analysis-of-parabolic-stochastic-PDEs/10.1214/19-AOS1841.short
5. Gordon-Rodriguez, E., Loaiza-Ganem, G., & Cunningham, J. (2020, November). The continuous categorical: a novel simplex-valued exponential family. In International Conference on Machine Learning (pp. 3637-3647). PMLR.
http://proceedings.mlr.press/v119/gordon-rodriguez20a.html
6. Pang, G., Alemayehu, D., de la Peña, V., & Klass, M. J. (2020). On the bias and variance of odds ratio, relative risk and false discovery proportion. Communications in Statistics-Theory and Methods, 1-31.
https://www.tandfonline.com/doi/abs/10.1080/03610926.2020.1867744
7. van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., … & Yau, C. (2021). Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1-26.
https://www.nature.com/articles/s43586-020-00001-2
8. Gu, Y., & Xu, G. (2021). A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis. Journal of the American Statistical Association, (just-accepted), 1-39.
https://www.tandfonline.com/doi/abs/10.1080/01621459.2021.1955689
9. Suk, J., & Kpotufe, S. (2021, March). Self-Tuning Bandits over Unknown Covariate-Shifts. In Algorithmic Learning Theory (pp. 1114-1156). PMLR.
http://proceedings.mlr.press/v132/suk21a.html
10. Li, H., Aue, A., Paul, D., Peng, J., & Wang, P. (2020). An adaptable generalization of Hotelling’s $ T^{2} $ test in high dimension. The Annals of Statistics, 48(3), 1815-1847.
11. Lo, S. H., & Yin, Y. (2021). An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray Images. arXiv preprint arXiv:2106.06911.
https://arxiv.org/abs/2106.06911
12. Tang, X., Wang, Z., He, Q., Liu, J., & Ying, Z. (2020). Latent feature extraction for process data via multidimensional scaling. psychometrika, 85(2), 378-397.
https://link.springer.com/article/10.1007/s11336-020-09708-3
13. Ghosal, P., & Mukherjee, S. (2020). Joint estimation of parameters in Ising model. The Annals of Statistics, 48(2), 785-810.
14. Nutz, M., San Martin, J., & Tan, X. (2020). Convergence to the mean-field game limit: a case study. The Annals of Applied Probability, 30(1), 259-286.
15. Pakman, A., Wang, Y., Mitelut, C., Lee, J., & Paninski, L. (2020, November). Neural clustering processes. In International Conference on Machine Learning (pp. 7455-7465). PMLR.
http://proceedings.mlr.press/v119/pakman20a.html
16. Jarrow, R., & Protter, P. (2020). Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.
https://onlinelibrary.wiley.com/doi/abs/10.1111/irfi.12239
17. Rush, C. (2020, June). An asymptotic rate for the LASSO loss. In International Conference on Artificial Intelligence and Statistics (pp. 3664-3673). PMLR.
http://proceedings.mlr.press/v108/rush20a.html
18. Deb, N., & Sen, B. (2021). Multivariate rank-based distribution-free nonparametric testing using measure transportation. Journal of the American Statistical Association, (just-accepted), 1-45.
https://www.tandfonline.com/doi/abs/10.1080/01621459.2021.1923508
19. Gonon, L., Muhle‐Karbe, J., & Shi, X. (2021). Asset pricing with general transaction costs: Theory and numerics. Mathematical Finance, 31(2), 595-648.
https://onlinelibrary.wiley.com/doi/abs/10.1111/mafi.12297
20. Sobel, M. E., & Lindquist, M. A. (2020). Estimating causal effects in studies of human brain function: New models, methods and estimands. The annals of applied statistics, 14(1), 452.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078549/
21. Tavaré, S. (2020). A note on the Screaming Toes game. arXiv preprint arXiv:2006.04805.
https://arxiv.org/abs/2006.04805
22. Aue, A., & Van Delft, A. (2020). Testing for stationarity of functional time series in the frequency domain. The Annals of Statistics, 48(5), 2505-2547.
23. Tagasovska, N., Chavez-Demoulin, V., & Vatter, T. (2020, November). Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery. In International Conference on Machine Learning (pp. 9311-9323). PMLR.
http://proceedings.mlr.press/v119/tagasovska20a.html
24. Obłój, J., & Wiesel, J. (2021). Robust estimation of superhedging prices. The Annals of Statistics, 49(1), 508-530.
25. Li, X., Chen, Y., Chen, X., Liu, J., & Ying, Z. (2021). Optimal stopping and worker selection in crowdsourcing: An adaptive sequential probability ratio test framework. Statistica Sinica, 31(1), 519-546.
http://eprints.lse.ac.uk/100873/
26. Wang, S., & Yuan, M. (2021). Revisiting colocalization via optimal transport. Nature Computational Science, 1(3), 177-178.
https://www.nature.com/articles/s43588-021-00046-7
27. Tang, C., Uriarte, M., Jin, H., C Morton, D., & Zheng, T. (2021). Large‐scale, image‐based tree species mapping in a tropical forest using artificial perceptual learning. Methods in Ecology and Evolution, 12(4), 608-618.
https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13549