政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/59434
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文筆數/總筆數 : 114992/146039 (79%)
造訪人次 : 54064333      線上人數 : 18
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    政大機構典藏 > 理學院 > 應用數學系 > 學位論文 >  Item 140.119/59434
    請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/59434


    題名: 修正條件分配勝率矩陣時最佳參考點之選取方法
    The best reference point method for the modification of the conditional distribution odds ratio matrices
    作者: 郭俊佑
    貢獻者: 姜志銘
    宋傳欽

    郭俊佑
    關鍵詞: 勝率矩陣
    相容
    條件機率矩陣
    參考點
    odds ratio matrix
    compatibility
    conditional probability matrix
    reference point
    日期: 2012
    上傳時間: 2013-09-02 16:46:19 (UTC+8)
    摘要: Chen(2010)提出如何用勝率函數來判斷給定的連續條件分配是否相容,以及
    相容時如何求對應的聯合分配。本研究提出,在二維有限的情形下,如何用勝率
    矩陣來判斷給定的條件機率矩陣是否相容,以及相容時如何求對應的聯合機率矩
    陣。又給定的條件機率矩陣不相容時,我們介紹了四種修改勝率矩陣的方法,同
    時在使用幾何平均法調整勝率矩陣的過程中,也發現選取最佳參考點以獲得最佳
    近似聯合機率矩陣之方法,並且給予理論證明。最後以模擬的方式發現,在修改
    勝率矩陣的四種方法中,以幾何平均法所得到的近似聯合機率矩陣,其條件機率
    矩陣最常接近所給定的條件機率矩陣。
    Chen (2010) provides the representations of odds ratio function to examine the compatibility of conditional probability density functions and gives the corresponding
    joint probability density functions if they are compatible. In this research, we provide the representations of odds ratio matrix to examine the compatibility of two discrete
    conditional probability matrices and give the corresponding joint probability matrix if they are compatible. For incompatible situations, we offer four methods to revise odds ratio matrices to find near joint probability matrices so that their conditional probability matrices are not far from the two given ones. That is, we provide four methods so that the sums of error squares are small. For each method, the sum of error squares may depend on the same reference point of two odds ratio matrices. We first
    discover by example that only the geometric method out of these four methods has a pattern to get the best reference point so that the sum of error squares is smallest. We
    then prove this finding in general. In addition, through simulation results, the geometric method would provide the smallest sum of error squares most often among these four methods. Hence, we suggest using geometric method. Its strategy to find the best reference point is also given.
    參考文獻: Chen, Hua Yun. (2010) Compatibility of conditionally specified models. Statistics and
    Probability Letters, 80, 670-677.
    Ip, Edward H., Wang, Yuchung J. (2009) Canonical representation of conditionally
    specified multivariate discrete distributions. Journal of Multivariate Analysis,100,
    1282-1290.
    描述: 碩士
    國立政治大學
    應用數學系數學教學碩士在職專班
    99972003
    101
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0099972003
    資料類型: thesis
    顯示於類別:[應用數學系] 學位論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    200301.pdf5757KbAdobe PDF2467檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋