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dc.contributor.authorZeger, Scott L.
dc.contributor.authorLiang, Kung-Yee
dc.date.accessioned2008-12-11T22:04:55Z
dc.date.available2008-12-11T22:04:55Z
dc.date.issued1995
dc.identifier.citationLiang, Kung-Yee and Scott L. Zeger. "Inference Based on Estimating Functions in the Presence of Nuisance Parameters" Statistical Science 10(2)(1995): 158-173 http://projecteuclid.org/euclid.ss/1177010028en
dc.identifier.urihttp://jhir.library.jhu.edu/handle/1774.2/32864
dc.description.abstractIn many studies, the scientific objective can be formulated in terms of a statistical model indexed by parameters, only some of which are of scientific interest. The other "nuisance parameters" are required to complete the specification of the probability mechanism but are not of intrinsic value in themselves. It is well known that nuisance parameters can have a profound impact on inference. Many approaches have been proposed to eliminate or reduce their impact. In this paper, we consider two situations: where the likelihood is completely specified; and where only a part of the random mechanism can be reasonably assumed. In either case, we examine methods for dealing with nuisance parameters from the vantage point of parameter estimating functions. To establish a context, we begin with a review of the basic concepts and limitations of optimal estimating functions. We introduce a hierarchy of orthogonality conditions for estimating functions that helps to characterize the sensitivity of inferences to nuisance parameters. It applies to both the fully and partly parametric cases. Throughout the paper, we rely on examples to illustrate the main ideas.en
dc.language.isoen_USen
dc.publisherInstitute of Mathematical Statisticsen
dc.subjectOrthogonalityen
dc.subjectOptimalityen
dc.subjectNuisance parameteren
dc.subjectEstimating functionen
dc.subjectConditional score functionen
dc.titleInference Based on Estimating Functions in the Presence of Nuisance Parametersen
dc.typeArticleen


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