Exact uniformly most powerful postselection confidence distributions |
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| Authors: | Andrea C. Garcia-Angulo Gerda Claeskens |
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| Affiliation: | ORStat and Leuven Statistics Research Centre, KU Leuven, Leuven, Belgium |
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| Abstract: | A conditioning on the event of having selected one model from a set of possibly misspecified normal linear regression models leads to the construction of uniformly optimal conditional confidence distributions. They can be used for valid postselection inference. The constructed conditional confidence distributions are finite sample exact and encompass all information regarding the focus parameter in the selected model. This includes the construction of optimal postselection confidence intervals at all significance levels and uniformly most powerful hypothesis tests. |
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| Keywords: | confidence distribution confidence interval linear model model selection postselection inference selective inference sufficiency |
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