Confidence Intervals for Asbestos Fiber Counts: Approximate Negative Binomial Distribution.
The negative binomial distribution is adopted for analyzing asbestos fiber counts so as to account for both the sampling errors in capturing only a finite number of fibers and the inevitable human variation in identifying and counting sampled fibers. A simple approximation to this distribution is de...
| Publicado en: | Annals of Work Exposures & Health Vol. 61; no. 2; pp. 237 - 248 |
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| Autores principales: | , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121476037&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121476037 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23987308 KJ1E jtl: Annals of Work Exposures & Health issn: 23987308 maglogo: N pubinfo: dt: Mar2017 vid: 61 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 121476037 121476037 121476037 10.1093/annweh/wxw020 121476037 ppf: 237 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Confidence Intervals for Asbestos Fiber Counts: Approximate Negative Binomial Distribution. aug: au: Bartley, David Slaven, James Harper, Martin affil: Department of Biostatistics, Indiana University School of Medicine, 340 W 10th St #6200, Indianapolis, IN 46202, USA sug: subj: Asbestos Quality Control (Technology) Methods Confidence Intervals Utilization Error of Severity Poisson Distribution Confidence Intervals Computer Simulation ab: The negative binomial distribution is adopted for analyzing asbestos fiber counts so as to account for both the sampling errors in capturing only a finite number of fibers and the inevitable human variation in identifying and counting sampled fibers. A simple approximation to this distribution is developed for the derivation of quantiles and approximate confidence limits. The success of the approximation depends critically on the use of Stirling's expansion to sufficient order, on exact normalization of the approximating distribution, on reasonable perturbation of quantities from the normal distribution, and on accurately approximating sums by inverse-trapezoidal integration. Accuracy of the approximation developed is checked through simulation and also by comparison to traditional approximate confidence intervals in the specific case that the negative binomial distribution approaches the Poisson distribution. The resulting statistics are shown to relate directly to early research into the accuracy of asbestos sampling and analysis. Uncertainty in estimating mean asbestos fiber concentrations given only a single count is derived. Decision limits (limits of detection) and detection limits are considered for controlling false-positive and false-negative detection assertions and are compared to traditional limits computed assuming normal distributions. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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