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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/41349

    Title: Bayesian nonparametrics for compliance to exposure standards
    Authors: Symons, Michael J.;陳主智;Chen, Chu-chih;Flynn, Michael R.
    Contributors: 淡江大學數學學系
    Keywords: Nuisance parameters;Predictive distributions
    Date: 1993-12
    Issue Date: 2010-01-28 07:21:40 (UTC+8)
    Publisher: American Statistical Association
    Abstract: A Bayesian nonparametric view of compliance to occupational standards is achieved through predictive distributions. The common assumption of lognormality of environmental exposures is relaxed while recognizing the practicality of a finite number of possible samples. These probability of compliance calculations are conditional on observing some of the samples. Familiar binomial and normal modes are identified with the classical perspective as limits of Bayesian nonparametric and parametric strategies, when the number of observed samples increases. In this situation, extensive previous sample data provide a correspondence between the classical and Bayesian approaches, rather than little or no previous information. Using an example, alternative procedures are illustrated and compared. Currently used methodology can be anti-conservative for protecting employees.
    Relation: Journal of the American Statistical Association 88(424), pp.1237-1241
    Appears in Collections:[Graduate Institute & Department of Mathematics] Journal Article

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