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銜接性試驗下最大概似法之偏差修正 = Biased Adjustment...

國立高雄大學統計學研究所

銜接性試驗下最大概似法之偏差修正 = Biased Adjustment for Bridging Studies Through Maximum Likelihood Technique

紀錄類型:	書目-語言資料,印刷品 : 單行本
並列題名:	Biased Adjustment for Bridging Studies Through Maximum Likelihood Technique
作者:	沈孝穎,
其他團體作者:	國立高雄大學
出版地:	[高雄市]
出版者:	撰者;
出版年:	2008[民97]
面頁冊數:	38面圖，表 : 30公分;
標題:	發表性誤差
標題:	Publication bias
電子資源:	http://handle.ncl.edu.tw/11296/ndltd/78926606394185877556
附註:	指導教授：黃錦輝
附註:	參考書目：面14
摘要註:	一般而言我們通常對於結果為顯著的實驗較為感興趣，也因此傾向公開發表檢定為顯著的實驗結果。由於這樣的傾向而使其發表時的實驗數據均為截斷(truncation)過後的數據，可能因此而有發表性誤差(publication bias)的產生。當我們需要引用發表的數據時，必須謹慎小心而不使估計產生偏差。本文中我們嘗試在銜接性試驗下，引用一筆檢定結果為顯著的數據，並以簡單線性迴歸模型與最大概似估計法來修正偏差，進而增加估計上的精準度。 Efficiency is highly relative to the number of information, andtherefore we may improve our efficiency by referring the publishedresults. However, we should carefully use this kind of information,since publication bias. Publication bias arises because we oftenpublish the significant results than non-significant conclusion, andhence, the published data are truncated by alternative hypothesis.It tends to give a tendency of over-estimate or under-estimate in along run. In this paper, we propose a method of using simple linearregression model and maximum likelihood technique to adjust thepublication bias while merging the truncated data under bridgingstudy in order to make our estimation more efficient.

銜接性試驗下最大概似法之偏差修正 = Biased Adjustment for Bridging Studies Through Maximum Likelihood Technique
沈, 孝穎

銜接性試驗下最大概似法之偏差修正 = Biased Adjustment for Bridging Studies Through Maximum Likelihood Technique / 沈孝穎撰 - [高雄市] : 撰者, 2008[民97]. - 38面 ; 圖，表 ; 30公分.
指導教授：黃錦輝參考書目：面14.
發表性誤差Publication bias

銜接性試驗下最大概似法之偏差修正 = Biased Adjustment for Bridging Studies Through Maximum Likelihood Technique
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