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Transformation-based model averaged tail area inference

Wei Yu, Wangli Xu (wxu.stat@gmail.com) and Lixing Zhu

Computational Statistics, 2014, vol. 29, issue 6, 1713-1726

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

Keywords: Confidence interval; Model averaging; Transformation-based model averaged tail area (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1007/s00180-014-0514-1

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