Empirical likelihood confidence intervals for the endpoint of a distribution function

Deyuan Li; Liang Peng; Yongcheng Qi

doi:10.1007/s11749-010-0204-4

Empirical likelihood confidence intervals for the endpoint of a distribution function

Deyuan Li, Liang Peng, Yongcheng Qi

Mathematics & Statistics

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

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)
Pages (from-to)	353-366
Number of pages	14
Journal	Test
Volume	20
Issue number	2
DOIs	https://doi.org/10.1007/s11749-010-0204-4
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

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title = "Empirical likelihood confidence intervals for the endpoint of a distribution function",

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.",

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AU - Li, Deyuan

AU - Peng, Liang

AU - Qi, Yongcheng

N1 - 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.

PY - 2011/8

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N2 - 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.

AB - 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.

KW - Confidence interval

KW - Coverage probability

KW - Empirical likelihood method

KW - Endpoint

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JF - Test

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ER -

Empirical likelihood confidence intervals for the endpoint of a distribution function

Abstract

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Keywords

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