期刊論文
| 學年 | 82 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 1993-12-01 |
| 作品名稱 | Bayesian nonparametrics for compliance to exposure standards |
| 作品名稱（其他語言） | |
| 著者 | Symons, Michael J.; 陳主智; Chen, Chu-chih; Flynn, Michael R. |
| 單位 | 淡江大學數學學系 |
| 出版者 | American Statistical Association |
| 著錄名稱、卷期、頁數 | Journal of the American Statistical Association 88(424), pp.1237-1241 |
| 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. |
| 關鍵字 | Nuisance parameters;Predictive distributions |
| 語言 | en_US |
| ISSN | 0162-1459 |
| 期刊性質 | 國外 |
| 收錄於 | |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/41349 ) |