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Exact Model Averaged Tail Area Confidence Intervals

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Statistics and Data Science (RSSDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1150))

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Abstract

Properties of the model averaged tail area (MATA) confidence interval proposed by Turek and Fletcher (CSDA 2012) depend critically on the data-based weights assigned to each tail area equation. By restricting attention to weights based on exponentiating minus AIC/2 and other similar weights it is not possible to find a MATA confidence interval with the desired minimum coverage probability. In the simple scenario that there are two nested normal linear regression models over which we average, a weight function is proposed that results in a MATA interval with correct minimum coverage for many combinations of the known quantity that the coverage depends on. This weight function is shown to outperform current popular choices of weight functions for the MATA interval.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, La Trobe University, Bundoora, VIC, 3086, Australia

    Paul Kabaila

  2. School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Rheanna Mainzer

Authors
  1. Paul Kabaila
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  2. Rheanna Mainzer
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Corresponding author

Correspondence to Rheanna Mainzer .

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Editors and Affiliations

  1. La Trobe University, Bundoora, VIC, Australia

    Hien Nguyen

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© 2019 Springer Nature Singapore Pte Ltd.

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Kabaila, P., Mainzer, R. (2019). Exact Model Averaged Tail Area Confidence Intervals. In: Nguyen, H. (eds) Statistics and Data Science. RSSDS 2019. Communications in Computer and Information Science, vol 1150. Springer, Singapore. https://doi.org/10.1007/978-981-15-1960-4_18

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  • DOI: https://doi.org/10.1007/978-981-15-1960-4_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1959-8

  • Online ISBN: 978-981-15-1960-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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