Random weighting estimation of one-sided confidence intervals in discrete distributions

Yalin Jiao; Yongmin Zhong; Shesheng Gao; Bijan Shirinzadeh

doi:10.4018/ijimr.2011040102

Random weighting estimation of one-sided confidence intervals in discrete distributions

Yalin Jiao, Yongmin Zhong, Shesheng Gao, Bijan Shirinzadeh

Mechanical & Aerospace Engineering

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

6 Citations (Scopus)

Abstract

This paper presents a new random weighting method for estimation of one-sided confidence intervals in discrete distributions. It establishes random weighting estimations for the Wald and Score intervals. Based on this, a theorem of coverage probability is rigorously proved by using the Edgeworth expansion for random weighting estimation of the Wald interval. Experimental results demonstrate that the proposed random weighting method can effectively estimate one-sided confidence intervals, and the estimation accuracy is much higher than that of the bootstrap method.

Original language	English
Pages (from-to)	18-26
Number of pages	9
Journal	International Journal of Intelligent Mechatronics and Robotics
Volume	1
Issue number	2
DOIs	https://doi.org/10.4018/ijimr.2011040102
Publication status	Published - Apr 2011

Keywords

Coverage probability
Edgeworth expansion
One-side confidence intervals
Random weighting estimation
Wald and Score intervals

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10.4018/ijimr.2011040102Licence: Unspecified

Cite this

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abstract = "This paper presents a new random weighting method for estimation of one-sided confidence intervals in discrete distributions. It establishes random weighting estimations for the Wald and Score intervals. Based on this, a theorem of coverage probability is rigorously proved by using the Edgeworth expansion for random weighting estimation of the Wald interval. Experimental results demonstrate that the proposed random weighting method can effectively estimate one-sided confidence intervals, and the estimation accuracy is much higher than that of the bootstrap method.",

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AU - Zhong, Yongmin

AU - Gao, Shesheng

AU - Shirinzadeh, Bijan

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AB - This paper presents a new random weighting method for estimation of one-sided confidence intervals in discrete distributions. It establishes random weighting estimations for the Wald and Score intervals. Based on this, a theorem of coverage probability is rigorously proved by using the Edgeworth expansion for random weighting estimation of the Wald interval. Experimental results demonstrate that the proposed random weighting method can effectively estimate one-sided confidence intervals, and the estimation accuracy is much higher than that of the bootstrap method.

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KW - Edgeworth expansion

KW - One-side confidence intervals

KW - Random weighting estimation

KW - Wald and Score intervals

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JF - International Journal of Intelligent Mechatronics and Robotics

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ER -

Random weighting estimation of one-sided confidence intervals in discrete distributions

Abstract

Keywords

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Research output

Random weighting estimation of one-sided confidence intervals in discrete distributions

Cite this