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

Yalin Jiao, Yongmin Zhong, Shesheng Gao, Bijan Shirinzadeh

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6 Citations (Scopus)


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 languageEnglish
Pages (from-to)18-26
Number of pages9
JournalInternational Journal of Intelligent Mechatronics and Robotics
Issue number2
Publication statusPublished - Apr 2011


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

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