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CONFIDENCE INTERVALS CONSTRUCTED BY MODEL AVERAGING AND BOOTSTRAP SMOOTHING

Part of: Decision theory Linear inference, regression Parametric inference

Published online by Cambridge University Press:  11 September 2020

CHRISTEEN WIJETHUNGA Open the ORCID record for CHRISTEEN WIJETHUNGA [Opens in a new window]
CHRISTEEN WIJETHUNGA*
Affiliation:
Department of Mathematics and Statistics, La Trobe University, Bundoora, Victoria 3086, Australia
*
e-mail: [email protected]
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Abstract

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Keywords

confidence intervalmodel averaged estimatorbootstrap smoothed estimatorcoverage probabilityscaled expected length

MSC classification

Primary: 62F25: Tolerance and confidence regions
Secondary: 62C99: None of the above, but in this section 62F40: Bootstrap, jackknife and other resampling methods 62J07: Ridge regression; shrinkage estimators
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 103 , Issue 2 , April 2021 , pp. 341 - 342
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Copyright
© 2020 Australian Mathematical Publishing Association Inc.

Footnotes

Thesis submitted to La Trobe University in September 2019; degree approved on 11 December 2019; supervisor Paul Kabaila, co-supervisor David Farchione.

References

Buckland, S. T., Burnham, K. P. and Augustin, N. H., ‘Model selection: an integral part of inference’, Biometrics 53 (1997), 603–618.CrossRefGoogle Scholar
Efron, B., ‘Estimation and accuracy after model selection’, J. Am. Stat. Assoc. 109 (2014), 991–1007.CrossRefGoogle ScholarPubMed
Kabaila, P. and Kong, Y., ‘Lower bounds on integrated risk, subject to inequality constraints’, Aust. N. Z. J. Stat. 58 (2016), 293–315.CrossRefGoogle Scholar