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dc.contributor.author張瑋華en_US
dc.contributor.authorWei Hwa Changen_US
dc.contributor.author陳志榮en_US
dc.contributor.authorChih Rung Chenen_US
dc.date.accessioned2014-12-12T02:20:14Z-
dc.date.available2014-12-12T02:20:14Z-
dc.date.issued1998en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT870337010en_US
dc.identifier.urihttp://hdl.handle.net/11536/63999-
dc.description.abstractIn this paper, we first extend some classical criteria for characterizing any finite-sample optimal estimating functions for the parameter of interest in a more general setting. Secondly, we give appropriate sufficient conditions for the existence of any finite-sample optimal estimating function for the parameter of interest in linear or non-orthogonal models. Finally, the example of megalithic stone rings (Angell and Barber, 1977) is discussed thoroughly to illustrate the theory.zh_TW
dc.description.abstractIn this paper, we first extend some classical criteria for characterizing any finite-sample optimal estimating functions for the parameter of interest in a more general setting. Secondly, we give appropriate sufficient conditions for the existence of any finite-sample optimal estimating function for the parameter of interest in linear or non-orthogonal models. Finally, the example of megalithic stone rings (Angell and Barber, 1977) is discussed thoroughly to illustrate the theory.en_US
dc.language.isoen_USen_US
dc.subjectfinite-sample optimalzh_TW
dc.subjectestimating functionzh_TW
dc.subjectscorezh_TW
dc.subjectgeneralized inversezh_TW
dc.subjectquasi-scorezh_TW
dc.subjectlinear modelzh_TW
dc.subjectnon-orthogonal modelzh_TW
dc.subjectfinite-sample optimalen_US
dc.subjectestimating functionen_US
dc.subjectscoreen_US
dc.subjectgeneralized inverseen_US
dc.subjectquasi-scoreen_US
dc.subjectlinear modelen_US
dc.subjectnon-orthogonal modelen_US
dc.title估計函數zh_TW
dc.titleFinite-Sample Optimal Estimating Functions in the Presence of a Nuisance Parameteren_US
dc.typeThesisen_US
dc.contributor.department統計學研究所zh_TW
Appears in Collections:Thesis


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