Abstract / truncated to 115 words (read the full abstract)

Post-selection estimation refers to the scenario where a preliminary data-based selection stage determines the specific estimation problem. In this work, we are researching two fundamental problems of this framework: estimation after model selection and estimation after parameter selection. In estimation after model selection, the observation model is unknown. Therefore, prior to estimation, a model selection procedure is used to chose a model from a set of candidate models. Then, the parameters of the selected model are estimated. In estimation after parameter selection, the observations model is known. In this case, the selection refers to choosing the ``parameters of interest'' based on the data, while the rest of the unknown parameters are considered as nuisance parameters. ... toggle 11 keywords

bayesian framework cram´er-rao bound lehmann-unbiasedness lower bounds mean-squared-error model misspecification model-selection non-bayesian framework parameter estimation parameter selection selective inference

Information

Author
Harel, Nadav
Institution
Ben-Gurion University of the Negev
Supervisors
Publication Year
2024
Upload Date
Dec. 31, 2024

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