Neyman-Pearson and Bayes interval estimates for sampling by attributes

Journal Article · Sat Dec 01 00:00:00 EST 1984 · IEEE Trans. Nucl. Sci.; (United States)

DOI:https://doi.org/10.1109/TNS.1984.4333554· OSTI ID:5844513

Mason, R M; Ryl, P; Williams, J W

This paper compares confidence intervals for single and multistage sampling schemes with Bayesian interval estimates obtained with a uniform prior distribution. Examples are presented in graphical form for sampling by attributes from an infinite population, or from a finite population with replacement. A general proof is given that the Neyman-Pearson confidence level associated with a confidence interval for the binomial parameter p will be no greater than the Bayesian confidence level calculated using a uniform prior distribution. A demonstration is provided for a fact published earlier, viz., that the Bayesian prior distribution can be selected so as to provide equality between one-sided Neyman-Pearson and Bayesian confidence bounds. Applications to EMP analysis are discussed in the final section.

• Research Organization: Booz Allen and Hamilton Inc., 2309 Renard Place, S.E., Suite 301, Albuquerque, NM 87106
• OSTI ID: 5844513
• Journal Information: IEEE Trans. Nucl. Sci.; (United States), Vol. NS-31:6
• Country of Publication: United States
• Language: English

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