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Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/36658
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dc.contributor.advisor鄭宗記zh_TW
dc.contributor.author范少華zh_TW
dc.creator范少華zh_TW
dc.date2002en_US
dc.date.accessioned2009-09-18T11:08:47Z-
dc.date.available2009-09-18T11:08:47Z-
dc.date.issued2009-09-18T11:08:47Z-
dc.identifierG0090354008en_US
dc.identifier.urihttps://nccur.lib.nccu.edu.tw/handle/140.119/36658-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description統計研究所zh_TW
dc.description90354008zh_TW
dc.description91zh_TW
dc.description.abstractAtkinson 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。zh_TW
dc.description.abstractAtkinson and Riani (2001) apply the forward search algorithm to deal with the problem of the detection of multiple outliers in binomial data.\r\nIn 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\r\nfill-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.en_US
dc.description.tableofcontentsChapter 1 Introduction\r\nChapter 2 Logistic Regression Model\r\nChapter 3 Robust Statistics\r\nChapter 4 Missing Values\r\nChapter 5 Robust Diagnostics and Missing Values\r\nChapter 6 Conclusionszh_TW
dc.language.isoen_US-
dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0090354008en_US
dc.subjectEM algorithmen_US
dc.subjectIncomplete dataen_US
dc.subjectgeneralized linear modelen_US
dc.subjecthigh breakdown ppinten_US
dc.subjectrobust methodsen_US
dc.titleRobust Diagnostics for the Logistic Regression Model With Incomplete Datazh_TW
dc.typethesisen
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