| dc.contributor.advisor | 鄭宗記 | zh_TW |
| dc.contributor.author (Authors) | 范少華 | zh_TW |
| dc.creator (作者) | 范少華 | zh_TW |
| dc.date (日期) | 2002 | en_US |
| dc.date.accessioned | 18-Sep-2009 19:08:47 (UTC+8) | - |
| dc.date.available | 18-Sep-2009 19:08:47 (UTC+8) | - |
| dc.date.issued (上傳時間) | 18-Sep-2009 19:08:47 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0090354008 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/36658 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 90354008 | zh_TW |
| dc.description (描述) | 91 | zh_TW |
| dc.description.abstract (摘要) | 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。 | zh_TW |
| dc.description.abstract (摘要) | 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. | en_US |
| dc.description.tableofcontents | Chapter 1 Introduction Chapter 2 Logistic Regression Model Chapter 3 Robust Statistics Chapter 4 Missing Values Chapter 5 Robust Diagnostics and Missing Values Chapter 6 Conclusions | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0090354008 | en_US |
| dc.subject (關鍵詞) | EM algorithm | en_US |
| dc.subject (關鍵詞) | Incomplete data | en_US |
| dc.subject (關鍵詞) | generalized linear model | en_US |
| dc.subject (關鍵詞) | high breakdown ppint | en_US |
| dc.subject (關鍵詞) | robust methods | en_US |
| dc.title (題名) | Robust Diagnostics for the Logistic Regression Model With Incomplete Data | zh_TW |
| dc.type (資料類型) | thesis | en |
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