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Post-selection inference

Thesis type
(Project) M.Sc.
Date created
2021-04-21
Authors/Contributors
Author: Tu, Yunwei
Abstract
Forward Stepwise Selection is a widely used model selection algorithm. It is, however, hard to do inference for a model that is already cherry-picked. A post-selection inference method called selective inference is investigated. Beginning with very simple examples and working towards more complex ones, we evaluate the method's performance in terms of its power and coverage probability though a simulation study. The target of inference is investigated and the impact of the amount of information used to construct conditional conference intervals is investigated. To achieve the same level of coverage probability, the more conditions we use, the wider the Confidence Interval is -- the effect can be extreme. Moreover, we investigate the impact of multiple conditioning, as well as the importance of the normality assumption on which the underlying theory is based. For models with not very many parameters (p
Document
Identifier
etd21324
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Lockhart, Richard
Language
English
Download file Size
input_data\21399\etd21324.pdf 11.33 MB

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