廣義線性模型中之延伸校正分數函數

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Title:	廣義線性模型中之延伸校正分數函數
Other Titles:	Extended corrected-score in generalized linear models
Authors:	楊喬閔;Yang, Chiao-Min
Contributors:	淡江大學數學學系碩士班黃逸輝;Huang, Yih-Huei
Keywords:	測量誤差;校正分數;條件分數;延伸校正分數;誤差增量;measurement error;Corrected score;Conditional score;extended corrected-score;error augmentation
Date:	2012
Issue Date:	2013-04-13 11:11:13 (UTC+8)
Abstract:	當迴歸模型中的自變數有測量誤差時，有兩個常用的估計方法：校正分數 (Corrected Score) 以及條件分數 (Conditional Score)，但此兩種方法所需的假設條件限制了能應用的迴歸模型。在此我們提出延伸校正分數(Extended corrected-score)，它是利用兩個重複量測，將分數函數加權，使之易於找到不偏估計式，加以校正，最後再次加權，進而得到延伸校正分數函數。另外在沒有重複量測時，我們提出誤差增量方法 (Error Augmentation)，來提供所需的兩個替代變數。 When the covariates are measured with errors in a regression model, there are two major consistent estimation methods: the corrected score and the conditional score. Though these methods work well when applicable, there are many models for which there is no corrected score or conditional score. We propose an estimation method named extended corrected-score that has a wider range of applicable models. Our estimation method was developed based on the availability of replicates. When there is no replicate, we propose an error augmentation technique to generate two surrogates, and our extended corrected-score is applicable again.
Appears in Collections:	[Graduate Institute & Department of Mathematics] Thesis

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