現狀資料中的共變數有測量誤差時加法性風險模型的迴歸分析

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Title:	現狀資料中的共變數有測量誤差時加法性風險模型的迴歸分析
Other Titles:	Additive hazard regression with current status data and measurement error in covariates
Authors:	王俞才;Wang, Yu-tsai
Contributors:	淡江大學數學學系碩士班黃逸輝;Huang, Yih-huei
Keywords:	加法性風險模型;計數過程測量誤差;現狀資料;乘法性風險模型;Additive hazard;Counting process;measurement error;Current Status;Proportion hazard
Date:	2010
Issue Date:	2010-09-23 16:14:00 (UTC+8)
Abstract:	所謂的現狀資料是指只能觀察到失效時間是發生在一個隨機的監控時間之前或之後。這樣的資料大多出現在生物醫學、經濟學或是社會學等研究當中。另一方面在蒐集資料時經常受限於儀器的精準度或量測方式的限制等因素而導致共變數無法準確測量。有鑑於此，本文探討在加法性風險模型之下影響失敗時間分佈的共變數含有測量誤差時的分析方法：利用Lin等人(1998)的看法，我們首先將加法性風險模型的問題轉換成乘法性風險模型的問題，再利用右設限資料下乘法性風險模型的特性進行共變數有測量誤差時的分析方法。 Current status data arise when the only knowledge about the failure time of interest is whether the failure occurs before or after a random monitoring time. Such data are commonly encountered in biomedicine, economic, sociology and other scientific areas. Due to the accuracy of instruments or the limitation of measurements, we may not measure the covariates precisely. This paper constructs estimation under the additive hazards model and discusses the analysis when covariates are subject to measurement error. Under certain conditions on the monitoring time and consider a certain process, the additive hazards model for the failure time implies the proportional hazards model for the special process. The analysis when measurement error presents was then derived under the proportional hazards model.
Appears in Collections:	[Department of Applied Mathematics and Data Science] Thesis

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