English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 111206/142126 (78%)
Visitors : 48112993      Online Users : 459
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/99311
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/99311


    Title: 具遺漏值之連續與順序變數混合資料的馬氏距離估計
    Estimating of Mahalanobis distances for mixed continuous and ordinal data with missing values
    Authors: 黃品勝
    Contributors: 鄭宗記
    黃品勝
    Keywords: 馬氏距離
    遺漏值
    混合資料
    多重插補
    Mahalanobis distances
    missing value
    mixed data
    multiple imputation
    Date: 2016
    Issue Date: 2016-07-20 16:52:21 (UTC+8)
    Abstract: Bedrick, Lapodus和Powell(2000)提出利用常態潛在變數模型(normal latent variable model),估計連續與順序變數混合型資料(mixed data)馬氏距離(Mahalanobis Distance)的方法,在本論文中沿用相同方法來估計具遺漏值混合型資料馬氏距離,利用一般位置模型(general location model)進行多重插補(multiple imputation)的方法,藉由模擬資料與實例分析,來評估此方法用於處理估計具遺漏值混合型資料馬氏距離。
    Bedrick, Lapodus, and Powell(2000) apply the normal latent variable model to estimate the Mahalanobis distances for mixed continuous and ordinal data. In this thesis, we extend the similar idea by applying general location model and multiple imputation to estimate the Mahalanobis distances for mixed countinuous and ordinal data with missing value. Simulation and real data are used to evaluate the proposed method.
    Reference: Bar-Hen, A. and Daudin, J. J. (1995). Generalization of the Mahalanobis Distance in
    The Mixed Case. Journal of Multivariate Analysis, 53, 332-342

    Bedrick, E. J., Lapidus, J. and Powell, J. F. (2000). Estimating the Mahalanobis Dista-
    Nce from Mixed Continuous and Discrete Data. Biometric 56, 394-401.

    Byar, D. P., Green S. B. (1980). The choice of treatment for patients based on covari-
    ate information: application to prostate cancer. Bull du Cancer 67,477-490

    Dempster, A. P., Laird, M., Rubin, D. B. (1977). Maximum likelihood from incompl-
    Ete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B 39, 1-38

    De Maesschalck, R., Jouan-Rimbaud, D. and Massart, D. L. (2000). The Mahalanobis
    Distance, Chemometrics and Intelligent Laboratory Systems 50, 1-18

    Hunt L. and Jorgensen M. (1999). Mixture model clustering using the multimix progr-
    am. Australia and New Zealand Journal of Statistics 41,153-171

    Schafer J. L. (1977). Analysis of Incomplete Multivariate Data, CHAPMAN and HA-
    LL

    Kullback, S. (1959). Information Theory and Statistical. New-York: Dover.

    Krzanowski, W. J. (1983). Distance between population using mixed continuous and
    categorical variable. Biometrika 70, 235-243

    Kenne Pagiui, E. C. and Canale, A. (2014). Pairwise likelihood inference for multiva-
    Riate categorical responses. Technical Report, Department of Statistics, Univers-
    ity of Padua

    Little, R. J. A. and Rubin, D. B. (1989). The analysis of social science data with miss-
    ing values. Sociological Methods and Research, 18, pp. 292-326

    Many, B. F. J. (1994). Multivariate Statistical Method: A Prime, 2nd edition. New Yo-
    rk : Chapman amd Hall.

    Mahalanobis, P. C.(1936). On the generalized distance in statistics, Proceedings of
    the National Institute of Science India, 2, 49–55.

    McParland,D.and Gormley,I.C. (2014). Model base clustering for mixed data:cluster-
    MD.Technical,University College Dublin.

    Olkin, I. and Tate, R. F. (1961). Multivariate correlation models with mixed discrete
    and continuous variables. Annals of Mathematical Statistics 32,448-465

    Poon, W. Y. and Lee, S. Y. (1987). Maximum likelihood estimation of multivariate
    polychoric correlation coefficients. Psychometrika 52, 409-430.

    Rao, C. R (1973). Linear Statistic Inference and Its Applications, 2nd edition. New
    York :Wiley.

    Rubin, D. B. (1976). Inference and missing data. Biometrika 63, 581-592

    Rubin, D. B. (1987). Multiple Imputations for Nonresponse in Surveys. Wiley, New
    York

    Searle, S. R., Casella, G., and McCulloch, C. E. (1992). Variance Components. New
    York: Wiley.

    Scafer,J.L(1999). Multiple imputation: a primer. Statiscal methods in medical resear-
    ch, 8(1), 3-15
    Description: 碩士
    國立政治大學
    統計學系
    103354017
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0103354017
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

    Files in This Item:

    File SizeFormat
    index.html0KbHTML2427View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    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 ©   - Feedback