Post-Selection Estimation Theory (2024)
Abstract / truncated to 115 words
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. ...
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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