dc.contributor.advisor | 陳麗霞 | zh_TW |
dc.contributor.author (Authors) | 劉國輝 | zh_TW |
dc.creator (作者) | 劉國輝 | zh_TW |
dc.date (日期) | 2003 | en_US |
dc.date.accessioned | 17-Sep-2009 18:44:49 (UTC+8) | - |
dc.date.available | 17-Sep-2009 18:44:49 (UTC+8) | - |
dc.date.issued (上傳時間) | 17-Sep-2009 18:44:49 (UTC+8) | - |
dc.identifier (Other Identifiers) | G0089354010 | en_US |
dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/33893 | - |
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) | 89354010 | zh_TW |
dc.description (描述) | 92 | zh_TW |
dc.description.abstract (摘要) | 當反應變數是二元(binary)時,邏輯斯迴歸(logistic regression)可幫助我們建立解釋變數與反應變數間的關係。然而,一般在進行邏輯斯迴歸分析時,總是假設樣本資料是由簡單隨機抽樣(simple random sampling)所取得,亦即所有的樣本皆具有相同的抽樣權重(sampling weights)。不過,在實務上,許多的大型抽樣調查都是採用複雜抽樣方法(complex sampling method)來抽取樣本。例如:採用多階段抽樣(multistage sampling)結合分層抽樣(stratified sampling)或是群集抽樣(cluster sampling)的方式來進行抽樣。由於樣本不再是以簡單隨機抽樣所取得,因此,統計分析的方式可分為兩類:一類乃設計導向(design-based);另一類則為模式導向(model-based)。其中,若將抽樣調查的抽樣設計方式以及樣本的代表性與統計模式的估計或檢定等推論過程相結合,則其屬於設計導向之方式。反之,若忽略這些因素,則相當於視調查資料來自於簡單隨機樣本,仍遵循一般的程序進行分析,則稱之為模式導向。本論文旨在探討如何以設計導向的方法,進行複雜抽樣方法所取得樣本資料的邏輯斯迴歸分析。 | zh_TW |
dc.description.tableofcontents | 第一章 緒論 ………………………………………………………… 1第一節 研究動機與目的 ……………………………………… 1第二節 本文結構 ……………………………………………… 3第二章 複雜抽樣設計下的邏輯斯迴歸模式 ……………………… 4第一節 邏輯斯迴歸模式的基本概念 ………………………… 5第二節 牛頓法 ………………………………………………… 7第三節 以未整合資料建立邏輯斯迴歸模式 ………………… 9第四節 以整合資料建立邏輯斯迴歸模式 …………………… 19第三章 實例研究 …………………………………………………… 38第一節 抽樣方法與變數介紹 ………………………………… 38第二節 複雜抽樣設計下的邏輯斯迴歸模式 ………………… 40第三節 設計導向與模式導向下結果的比較 ………………… 48第四章 結論與建議 ………………………………………………… 52參考文獻 …………………………………………………………… 55附錄一 ……………………………………………………………… 57附錄二 ……………………………………………………………… 59附錄三 ……………………………………………………………… 60 | zh_TW |
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dc.language.iso | en_US | - |
dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0089354010 | en_US |
dc.subject (關鍵詞) | 複雜抽樣設計 | zh_TW |
dc.subject (關鍵詞) | 邏輯斯迴歸模式 | zh_TW |
dc.title (題名) | 複雜抽樣設計下邏輯斯迴歸模式之分析 | zh_TW |
dc.type (資料類型) | thesis | en |
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dc.relation.reference (參考文獻) | 3. Graubard, BI, Korn, EL & Midthune, D. (1997), Testing Goodness of Fit for Logistic Regression with Survey Data, National Cancer Institute. | zh_TW |
dc.relation.reference (參考文獻) | 4. Guttman, I. (1982), Linear Models: An Introduction. John Wiley & Sons, Inc. | zh_TW |
dc.relation.reference (參考文獻) | 5. Hosmer, DW & Lemeshow, S. (2000), Applied Logistic Regression. John Wiley, Inc., New York , pp. 147-149, 203-365. | zh_TW |
dc.relation.reference (參考文獻) | 6. Jesse, AC, Brian, DM & Joseph, AC (1995), Logitse : A SAS Marco for Logistic Regression Modeling in Complex Surveys. | zh_TW |
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dc.relation.reference (參考文獻) | 8. Lehtonen, R. & Pahkinen, EJ (1995), Practical Methods for Design and Analysis of Complex Surveys. John Wiley & Sons, Inc., New York, pp. 269-276. | zh_TW |
dc.relation.reference (參考文獻) | 9. Roberts, G., Rao, JNK. & Kumar, S. (1987), Logistic Regression Analysis of Sample Survey Data, Biomertrika, 74, 1, pp. 1-12. | zh_TW |
dc.relation.reference (參考文獻) | 10. Skinner, CJ, Holt, D. & Smith, TMF. (1989), Analysis of Complex Surveys. John Wily & Son, Inc., Singapore. | zh_TW |
dc.relation.reference (參考文獻) | 11. Stata Press (2001), STATA 7 Reference Su-Z. College Station, Texas. | zh_TW |
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dc.relation.reference (參考文獻) | 13. Thomas, AW (1991), Introduction to Linear Algebra. Blackie & Son, Inc, pp. 186. | zh_TW |
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