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A Reference Population-Based Conformance Proportion

Author

Listed:
  • Hsin-I Lee

    (National Taiwan University)

  • Hungyen Chen

    (The University of Tokyo)

  • Hirohisa Kishino

    (The University of Tokyo)

  • Chen-Tuo Liao

    (National Taiwan University)

Abstract

Conformance proportion is a useful index in agricultural, biological, and environmental applications, which is defined as the proportion that a characteristic of interest falls in an acceptance region of a specification. In practice, however, the acceptance region may not be available when employing the regular conformance proportion. In this article, we propose a new index in which the acceptance region is obtained from a reference population. Furthermore, we develop approaches to constructing confidence limits for the proposed conformance proportion using the concept of a fiducial generalized pivotal quantity. We first consider the simple situation that the characteristic is assumed to be a univariate normal random variable. Then we extend it to the one-way random effects model. The proposed method is evaluated through simulation study and real data analysis. It is shown that our proposed new index and its statistical inference are easy to implement and reasonably satisfactory for most applications. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Hsin-I Lee & Hungyen Chen & Hirohisa Kishino & Chen-Tuo Liao, 2016. "A Reference Population-Based Conformance Proportion," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 684-697, December.
    • Handle: RePEc:spr:jagbes:v:21:y:2016:i:4:d:10.1007_s13253-016-0268-z
    DOI: 10.1007/s13253-016-0268-z
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    References listed on IDEAS

    as
    1. Jan Hannig & Thomas C. M. Lee, 2009. "Generalized fiducial inference for wavelet regression," Biometrika, Biometrika Trust, vol. 96(4), pages 847-860.
    2. Hannig, Jan & Iyer, Hari & Patterson, Paul, 2006. "Fiducial Generalized Confidence Intervals," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 254-269, March.
    3. Jan Hannig & Hari Iyer & Randy C. S. Lai & Thomas C. M. Lee, 2016. "Generalized Fiducial Inference: A Review and New Results," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1346-1361, July.
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