On the Large-Sample Minimal Coverage Probability of Confidence Intervals After Model Selection

By Paul Kabaila, H. Leeb. 2006

85 citations recorded by the source corpus.

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In Statistics and Probability

Citations in the corpus

Model Selection (2018, background): “Problems related to the constructions of valid confidence intervals post model selection are discussed in Kabaila (1995, 1998), Pötscher (1995), Kabaila and Leeb (2006), Leeb (2007), and Pötscher (2007).”
On various confidence intervals post-model-selection (2014, unknown): “More precisely, let X be an n × 2 matrix of rank 2, and assume that M ] Arguably, any reasonable model selection procedure in this setting must be equivalent to a likelihood-ratio test, at least asymptotically; cf. Kabaila and Leeb (2006).”
Valid post-selection inference (2013, background): “By Richard Berk, Lawrence Brown1, Andreas Buja1, Kai Zhang1 and Linda Zhao1”
Uniformly valid confidence intervals post-model-selection (2016, background): “This confidence interval is developed for the lasso model selector and has some asymptotic guarantees which are discussed in Taylor and Tibshirani (2017).”
The Coverage Properties of Confidence Regions After Model Selection (2009, background): “This upper bound is a finite sample analogue of the large sample upper bound of Kabaila & Leeb (2006).”
Valid confidence intervals for post-model-selection predictors (2014, methodology): “…coverage probability of ‘naive’ conﬁdence sets can be dramatically smaller than the nominal one), and valid procedures are often hard to ﬁnd; see Leeb and P¨otscher (2005, 2006, 2012), Kabaila and Leeb (2006), P¨otscher (2009) and references therein for an introduction to the issues involved here.”