個數資料之過離散性的強韌推論; Inference for overdispersion in count data without making distributional assumptions

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/48245

Title:	個數資料之過離散性的強韌推論;Inference for overdispersion in count data without making distributional assumptions
Authors:	曹雅婷;Ya-ting Tsao
Contributors:	統計研究所
Keywords:	過離散性的個數資料;Bartlett第二等式;對數迴歸模型;Bartlett's second identity;over-dispersion count data;log regression model
Date:	2011-07-08
Issue Date:	2012-01-05 14:42:30 (UTC+8)
Abstract:	本文之目的在於利用，當估計模型假設錯誤時，Bartlett的第二等式不正確的性質，來提出一個估計具有過離散性的個數資料之過離散係數的方法。再根據Presnell與Boos(2004)在附錄所提出的方法來估計過離散係數估計量的變異數，並探討估計方法的有效性。論文中提出一個不需知道正確模型下估計過離散係數之方法，適用於對數迴歸模型或其他合理的迴歸模型。 This thesis provides a method for estimating the over-dispersion count data. And this method adopts the poisson distribution as the working model. The violation of the Bartlett’s second identity is then made use of to give rise to a useful formula for the estimation of the over-dispersion. This new means is applicable for any sensible link function that relates the response probabilities to the variates.
Appears in Collections:	[Graduate Institute of Statistics] Electronic Thesis & Dissertation

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