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A general framework to increase the robustness of model-based change point detection algorithms to outliers and noise.

, , , , , and . SDM, page 162-170. SIAM, (2016)

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Uncertainty Quantification in Inverse Models in Hydrology., , , , , , , and . CoRR, (2023)A Parameter-Free Spatio-Temporal Pattern Mining Model to Catalog Global Ocean Dynamics., , , , and . ICDM, page 151-160. IEEE Computer Society, (2013)Probabilistic Inverse Modeling: An Application in Hydrology., , , , , , , and . SDM, page 847-855. SIAM, (2023)Structural Equation Modeling, A Bayesian Approach.. Technometrics, 50 (3): 411-412 (2008)Tripoles: A New Class of Relationships in Time Series Data., , , , , , and . KDD, page 697-706. ACM, (2017)Another look at the jackknife: further examples of generalized bootstrap. Statistics & Probability Letters, 40 (4): 307--319 (Nov 15, 1998)Feature selection using e-values., and . ICML, volume 162 of Proceedings of Machine Learning Research, page 14753-14773. PMLR, (2022)Sparse Group Lasso: Consistency and Climate Applications., , , , and . SDM, page 47-58. SIAM / Omnipress, (2012)Generalized bootstrap for estimators of minimizers of convex functions, and . Journal of Statistical Planning and Inference, 117 (2): 225--239 (Dec 1, 2003)Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data., , , , and . Comput. Stat. Data Anal., (2020)