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On the least-squares model averaging interval estimator
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0003-4415-8734
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 47, no 1, p. 118-132Article in journal (Refereed) Published
Abstract [en]

In many applications of linear regression models, randomness due to model selection is commonly ignored in post-model selection inference. In order to account for the model selection uncertainty, least-squares frequentist model averaging has been proposed recently. We show that the confidence interval from model averaging is asymptotically equivalent to the confidence interval from the full model. The finite-sample confidence intervals based on approximations to the asymptotic distributions are also equivalent if the parameter of interest is a linear function of the regression coefficients. Furthermore, we demonstrate that this equivalence also holds for prediction intervals constructed in the same fashion.

Place, publisher, year, edition, pages
2018. Vol. 47, no 1, p. 118-132
Keywords [en]
Asymptotic equivalence, Linear model, Local asymptotics, Frequentist model averaging, Post-selection inference
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-328528DOI: 10.1080/03610926.2017.1300272ISI: 000418085500010OAI: oai:DiVA.org:uu-328528DiVA, id: diva2:1136009
Available from: 2017-08-25 Created: 2017-08-25 Last updated: 2018-01-17Bibliographically approved

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Ankargren, SebastianJin, Shaobo

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