數據依賴誤差之階梯函數迴歸的貝氏方法; A Bayesian Approach to Step Function Regression with Data Dependent Error

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/7943

Title:	數據依賴誤差之階梯函數迴歸的貝氏方法;A Bayesian Approach to Step Function Regression with Data Dependent Error
Authors:	陳孜圩;Tzu-yu Chen
Contributors:	數學研究所
Keywords:	變點問題;馬可夫鏈蒙地卡羅法;階梯函數;比較型基因體雜交分析技術;貝氏迴歸模型;Markov chain Monte Carlo;change point problem;comparative genomic hybridization;step function;Bayesian regression model
Date:	2008-06-06
Issue Date:	2009-09-22 11:09:45 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	在這篇論文中，我們將 Wen et al. 在2006年提出的貝氏迴歸模型做推廣，考慮其誤差項會受到迴歸函數的函數值所影響。除此之外，我們也更近一步地探討，在每一個位置附近發生跳點的機率以及其所跳的高度之間的關係。 One of the purposes of this thesis is to extend the Bayesian step-function regression model of Wen et al. (2006) to allow for more general noise term so that the error term may vary with the ratio itself, which is also a familiar phenomenon in regression analysis. The other purpose of this thesis is to explore in more detail the relation between the size of the jump at a point of the posterior mode and posterior probability that it is a jump point.
Appears in Collections:	[Graduate Institute of Mathematics] Electronic Thesis & Dissertation

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