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Transformation-based model averaged tail area inference

Author

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  • Wei Yu
  • Wangli Xu
  • Lixing Zhu

Abstract

In parameter estimation, it is not a good choice to select a “best model” by some criterion when there is model uncertainty. Model averaging is commonly used under this circumstance. In this paper, transformation-based model averaged tail area is proposed to construct confidence interval, which is an extension of model averaged tail area method in the literature. The transformation-based model averaged tail area method can be used for general parametric models and even non-parametric models. Also, it asymptotically has a simple formula when a certain transformation function is applied. Simulation studies are carried out to examine the performance of our method and compare with existing methods. A real data set is also analyzed to illustrate the methods. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Wei Yu & Wangli Xu & Lixing Zhu, 2014. "Transformation-based model averaged tail area inference," Computational Statistics, Springer, vol. 29(6), pages 1713-1726, December.
    • Handle: RePEc:spr:compst:v:29:y:2014:i:6:p:1713-1726
    DOI: 10.1007/s00180-014-0514-1
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    References listed on IDEAS

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    1. Turek, Daniel & Fletcher, David, 2012. "Model-averaged Wald confidence intervals," Computational Statistics & Data Analysis, Elsevier, vol. 56(9), pages 2809-2815.
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