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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/36658


    Title: Robust Diagnostics for the Logistic Regression Model With Incomplete Data
    Authors: 范少華
    Contributors: 鄭宗記
    范少華
    Keywords: EM algorithm
    Incomplete data
    generalized linear model
    high breakdown ppint
    robust methods
    Date: 2002
    Issue Date: 2009-09-18 19:08:47 (UTC+8)
    Abstract: Atkinson 及 Riani 應用前進搜尋演算法來處理百牡利資料中所包含的多重離群值(2001）。在這篇論文中，我們沿用相同的想法來處理在不完整資料下一般線性模型中的多重離群值。這個演算法藉由先填補資料中遺漏的部分，再利用前進搜尋演算法來確認資料中的離群值。我們所提出的方法可以解決處理多重離群值時常會遇到的遮蓋效應。我們應用了一些真實資料來說明這個演算法並得到令人滿意結果。
    Atkinson and Riani (2001) apply the forward search algorithm to deal with the problem of the detection of multiple outliers in binomial data.
    In this thesis, we extend the similar idea to identify multiple outliers for the generalized linear models when part of data are missing. The algorithm starts with imputation method to
    fill-in the missing observations in the data, and then use the forward search algorithm to confirm outliers. The proposed method can overcome the masking effect, which commonly occurs when multiple outliers exit in the data. Real data are used to illustrate the procedure, and satisfactory results are obtained.
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    Description: 碩士
    國立政治大學
    統計研究所
    90354008
    91
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0090354008
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

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