Abstract
Estimating the endpoint of a distribution function is of interest in product analysis and predicting the maximum lifetime of an item. In this paper, we propose an empirical likelihood method to construct a confidence interval for the endpoint. A simulation study shows the proposed confidence interval has better coverage accuracy than the normal approximation method, and bootstrap calibration improves the accuracy.
Original language | English (US) |
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Pages (from-to) | 353-366 |
Number of pages | 14 |
Journal | Test |
Volume | 20 |
Issue number | 2 |
DOIs | |
State | Published - Aug 2011 |
Bibliographical note
Funding Information:Acknowledgements We thank two reviewers for helpful comments. Li’s research was partly supported by NNSFC Grant 10801038. Peng’s research was supported by NSA Grant H98230-10-1-0170 and Qi’s research was supported by NSA Grant H98230-10-1-0161.
Keywords
- Confidence interval
- Coverage probability
- Empirical likelihood method
- Endpoint