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DTSTART:20070311T020000
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DTSTAMP:20211004T161911
DTSTART;TZID=America/Detroit:20211022T100000
DTEND;TZID=America/Detroit:20211022T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Gerda Claeskens\, Professor\, Research Centre for Operations Research and Statistics (ORSTAT)\, KU Leuven
DESCRIPTION:Abstract: When a model for a statistical analysis is not given before the analysis\, but is the result of a model search endeavor\, the uncertainty about the model that is used for inference has consequences for hypothesis testing and for the construction of confidence intervals for the model parameters of interest. Ignoring this uncertainty leads to overoptimistic results\, implying that computed p-values are too small and that confidence intervals are too narrow for the intended coverage.\nI will explain how to use confidence distributions to obtain valid inference after model selection for the parameters of interest. Under some assumptions\, uniformly most powerful post-selection confidence curves are obtained.\n\nThis is joint work with Andrea Garcia-Angulo.\n\nGerda Claeskens is a professor at the Research Centre for Operations Research and Statistics (ORSTAT)\, KU Leuven. Her research interests are Model selection and model averaging\; Post-selection inference\; Nonparametric regression.\n\nhttps://perswww.kuleuven.be/~u0043181/index.htm
UID:84421-21623923@events.umich.edu
URL:https://events.umich.edu/event/84421
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:Off Campus Location
CONTACT:
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