淡江大學機構典藏:Item 987654321/41349
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    题名: Bayesian nonparametrics for compliance to exposure standards
    作者: Symons, Michael J.;陳主智;Chen, Chu-chih;Flynn, Michael R.
    贡献者: 淡江大學數學學系
    关键词: Nuisance parameters;Predictive distributions
    日期: 1993-12
    上传时间: 2010-01-28 07:21:40 (UTC+8)
    出版者: American Statistical Association
    摘要: 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.
    關聯: Journal of the American Statistical Association 88(424), pp.1237-1241
    DOI: 
    显示于类别:[數學學系暨研究所] 期刊論文

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