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Random Weighting Estimation of One-Sided Confidence Intervals in Discrete Distributions

Random Weighting Estimation of One-Sided Confidence Intervals in Discrete Distributions

Yalin Jiao, Yongmin Zhong, Shesheng Gao, Bijan Shirinzadeh
Copyright: © 2013 |Pages: 11
ISBN13: 9781466636347|ISBN10: 1466636343|EISBN13: 9781466636354
DOI: 10.4018/978-1-4666-3634-7.ch006
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MLA

Jiao, Yalin, et al. "Random Weighting Estimation of One-Sided Confidence Intervals in Discrete Distributions." Advanced Engineering and Computational Methodologies for Intelligent Mechatronics and Robotics, edited by Shahin Sirouspour, IGI Global, 2013, pp. 92-102. https://doi.org/10.4018/978-1-4666-3634-7.ch006

APA

Jiao, Y., Zhong, Y., Gao, S., & Shirinzadeh, B. (2013). Random Weighting Estimation of One-Sided Confidence Intervals in Discrete Distributions. In S. Sirouspour (Ed.), Advanced Engineering and Computational Methodologies for Intelligent Mechatronics and Robotics (pp. 92-102). IGI Global. https://doi.org/10.4018/978-1-4666-3634-7.ch006

Chicago

Jiao, Yalin, et al. "Random Weighting Estimation of One-Sided Confidence Intervals in Discrete Distributions." In Advanced Engineering and Computational Methodologies for Intelligent Mechatronics and Robotics, edited by Shahin Sirouspour, 92-102. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3634-7.ch006

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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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