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On the boundedness and nonmonotonicity of generalized score statistics

Field, C A; Pang, Zhen; Welsh, Alan

Description

We show in the context of the linear regression model fitted by Gaussian quasi-likelihood estimation that the generalized score statistics of Boos and Hu and Kalbfleisch for individual parameters can be bounded and nonmonotone in the parameter, making it

dc.contributor.authorField, C A
dc.contributor.authorPang, Zhen
dc.contributor.authorWelsh, Alan
dc.date.accessioned2015-12-10T23:22:25Z
dc.identifier.issn0003-1305
dc.identifier.urihttp://hdl.handle.net/1885/66508
dc.description.abstractWe show in the context of the linear regression model fitted by Gaussian quasi-likelihood estimation that the generalized score statistics of Boos and Hu and Kalbfleisch for individual parameters can be bounded and nonmonotone in the parameter, making it
dc.publisherAmerican Statistical Association
dc.sourceThe American Statistician
dc.subjectConfidence intervals
dc.subjectEstimating equations
dc.subjectQuasi-likelihood estimation
dc.subjectScore test
dc.titleOn the boundedness and nonmonotonicity of generalized score statistics
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume66
dc.date.issued2012
local.identifier.absfor010400 - STATISTICS
local.identifier.ariespublicationf5625xPUB1296
local.type.statusPublished Version
local.contributor.affiliationField, C A, Dalhousie University
local.contributor.affiliationPang, Zhen, Nanyang Technological University
local.contributor.affiliationWelsh, Alan, College of Physical and Mathematical Sciences, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.issue2
local.bibliographicCitation.startpage92
local.bibliographicCitation.lastpage98
local.identifier.doi10.1080/00031305.2012.703888
dc.date.updated2016-02-24T08:43:47Z
local.identifier.scopusID2-s2.0-84865145873
local.identifier.thomsonID000308278300003
CollectionsANU Research Publications

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