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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/95946


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/95946


    Title: 失去部份訊息的類別資料之貝氏分析
    Bayesian analysis for censored categorical data
    Authors: 洪淑玲
    Contributors: 姜志銘
    洪淑玲
    Date: 1998
    Issue Date: 2016-05-10 16:00:44 (UTC+8)
    Abstract: 　　失去部份訊息的類別資料之貝氏分析，早期的學者大都是針對1.失去的部份訊息是無價值性，2.誠實報告，與3.類別機率與報告的條件機率，二者的先驗分佈彼此互相獨立的假設條件來研究，直至近期雖有一些研究去除部份條件，但仍含有某些限制條件。文中將從不同的角度切入，並且不需上述的三個限制條件，同時先驗分佈族也由過去大部份學者所討論的Dirichlet分佈族擴展到Generalized Dirichlet分佈族，使能更廣泛包含各種先驗訊息。對於後驗平均數的估計，我們提出三種不同的計算方式：直接計算法、分解法與Quasi-Bayes法，並以幾個例子來比較說明。
    　　Bayesian methods for censored categorical data have been researched for decades. However, most of those are based on three restrictions that were discussed in Dickey, Jiang, and Kadane (1987), that is, noninformatively censored, truthful reporting, and prior independence between the categorical probability and conditional reported probability. Although some restrictions have been relaxed by some recent works, none has considered the cases without any restrictions. In this research, we shall remove all restrictions and extend prior distribution family to Generalized Dirichlet distribution family. In addition, three computational approaches for posterior means of parameters of the sampling population are presented: directed method, decomposition method, and quasi-Bayes method. Examples are given to illustrate and compare methods.
    Description: 博士
    國立政治大學
    統計學系
    82354502
    Source URI: http://thesis.lib.nccu.edu.tw/record/#A2010000642
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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