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dc.contributor.authorVardeman, Stephen B.en
dc.contributor.authorLee, Chiang-Shengen
dc.date.accessioned2011-09-06T11:37:54Z-
dc.date.available2011-09-06T11:37:54Z-
dc.date.issued2003-
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
dc.language.isoengen
dc.publisher|aUniversität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen |cDortmunden
dc.relation.ispartofseries|aTechnical Report |x2003,39en
dc.subject.ddc519en
dc.titleLikelihood-based statistical estimation from quantized data-
dc.typeWorking Paperen
dc.identifier.ppn823416291en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:sfb475:200339en

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