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Bootstrap application to the lagrang...

Smith, David Johnston.

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Bootstrap application to the lagrange gamma II dose-response model.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Bootstrap application to the lagrange gamma II dose-response model./
作者:	Smith, David Johnston.
面頁冊數:	56 p.
附註:	Adviser: K. G. Janardan.
Contained By:	Masters Abstracts International40-03.
標題:	Health Sciences, Toxicology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1407059
ISBN:	0493441484

Bootstrap application to the lagrange gamma II dose-response model.
Smith, David Johnston.

Bootstrap application to the lagrange gamma II dose-response model. - 56 p.

Adviser: K. G. Janardan.

Thesis (M.A.)--Eastern Michigan University, 2001.

There are many factors affecting the certainty associated with determining safe levels of chemicals for humans. One of these is the mathematical uncertainty associated with the dose-response model. A lagrange gamma II dose-response model has been proposed for the analysis of toxicity, given experimental data on animals (Janardan 1995). This model assumes that a toxic response may be the result of multiple random or nonrandom biological events and attempts to assess the risk at low doses based on data generated at higher doses in laboratory studies. The parameters associated with this model must be estimated using a nonlinear iterative algorithm. Here, bootstrap methodology is applied using SAS<super>®</super> software to estimate the parameters, as well as the uncertainty surrounding these parameter estimates.

ISBN: 0493441484Subjects--Topical Terms:

1017752
Health Sciences, Toxicology.

Bootstrap application to the lagrange gamma II dose-response model.
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520 $a There are many factors affecting the certainty associated with determining safe levels of chemicals for humans. One of these is the mathematical uncertainty associated with the dose-response model. A lagrange gamma II dose-response model has been proposed for the analysis of toxicity, given experimental data on animals (Janardan 1995). This model assumes that a toxic response may be the result of multiple random or nonrandom biological events and attempts to assess the risk at low doses based on data generated at higher doses in laboratory studies. The parameters associated with this model must be estimated using a nonlinear iterative algorithm. Here, bootstrap methodology is applied using SAS<super>®</super> software to estimate the parameters, as well as the uncertainty surrounding these parameter estimates.
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