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.
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Acknowledgments
The research was supported by National Natural Science Foundation of China (No. 11071253), Beijing Nova Program (2010B066), and a GRF grant from the Reseach Grants Council of Hong Kong, China. The authors thank the editor, associate editor and referees for their constructive suggestions that led to an improvement of an early manuscript.
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Wangli Xu: The research described herein was supported by a grant from the University Grants Council of Hong Kong.
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Yu, W., Xu, W. & Zhu, L. Transformation-based model averaged tail area inference. Comput Stat 29, 1713–1726 (2014). https://doi.org/10.1007/s00180-014-0514-1
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DOI: https://doi.org/10.1007/s00180-014-0514-1