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The performance of model averaged tail area confidence intervals

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  • Paul Kabaila
  • A. H. Welsh
  • Rheanna Mainzer

Abstract

We investigate the exact coverage and expected length properties of the model averaged tail area (MATA) confidence interval proposed by Turek and Fletcher, CSDA, 2012, in the context of two nested, normal linear regression models. The simpler model is obtained by applying a single linear constraint on the regression parameter vector of the full model. For given length of response vector and nominal coverage of the MATA confidence interval, we consider all possible models of this type and all possible true parameter values, together with a wide class of design matrices and parameters of interest. Our results show that, while not ideal, MATA confidence intervals perform surprisingly well in our regression scenario, provided that we use the minimum weight within the class of weights that we consider on the simpler model.

Suggested Citation

  • Paul Kabaila & A. H. Welsh & Rheanna Mainzer, 2017. "The performance of model averaged tail area confidence intervals," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(21), pages 10718-10732, November.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:21:p:10718-10732
    DOI: 10.1080/03610926.2016.1242741
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    Cited by:

    1. Paul Kabaila & Nishika Ranathunga, 2021. "Computation of the expected value of a function of a chi-distributed random variable," Computational Statistics, Springer, vol. 36(1), pages 313-332, March.
    2. Shaobo Jin, 2022. "Frequentist Model Averaging in Structure Equation Model With Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1130-1145, September.

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