Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/49323
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dc.contributor.authorVardeman, Stephen B.en_US
dc.contributor.authorLee, Chiang-Shengen_US
dc.date.accessioned2011-09-06T11:37:54Z-
dc.date.available2011-09-06T11:37:54Z-
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/10419/49323-
dc.description.abstractMost standard statistical methods treat numerical data as if they were real (infinitenumber- of-decimal-places) observations. The issue of quantization or digital resolution is recognized by engineers and metrologists, but is largely ignored by statisticians and can render standard statistical methods inappropriate and misleading. This article discusses some of the difficulties of interpretation and corresponding difficulties of inference arising in even very simple measurement contexts, once the presence of quantization is admitted. It then argues (using the simple case of confidence interval estimation based on a quantized random sample from a normal distribution as a vehicle) for the use of statistical methods based on 'rounded data likelihood functions' as an effective way of dealing with the issue.en_US
dc.language.isoengen_US
dc.publisher|aUniv., SFB 475 |cDortmunden_US
dc.relation.ispartofseries|aTechnical Report // Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen |x2003,39en_US
dc.subject.ddc310en_US
dc.titleLikelihood-based statistical estimation from quantized dataen_US
dc.typeWorking Paperen_US
dc.identifier.ppn823416291en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:sfb475:200339-

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