| dc.contributor.advisor | 陳麗霞 | zh_TW |
| dc.contributor.author (作者) | 張簡嘉詠 | zh_TW |
| dc.creator (作者) | 張簡嘉詠 | zh_TW |
| dc.date (日期) | 2007 | en_US |
| dc.date.accessioned | 2009-09-14 | - |
| dc.date.available | 2009-09-14 | - |
| dc.date.issued (上傳時間) | 2009-09-14 | - |
| dc.identifier (其他 識別碼) | G0094354011 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30915 | - |
| 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) | 94354011 | zh_TW |
| dc.description (描述) | 96 | zh_TW |
| dc.description.abstract (摘要) | 競爭風險之下對邊際分配的估計，是許多領域中常遇到的問題。由於主要事件及次要事件互相競爭，只要一種事件先發生即終止對另一事件的觀察，在兩事件同時發生的機率為0之下，連一筆完整的資料我們都無法蒐集到。除非兩事件互為獨立或加上其它條件，否則會有邊際分配無法識別的問題。但是獨立的條件在有些情況下並不合理，為解決相依競爭風險之邊際分配無法識別的問題，可先假定兩事件發生時間之間的關係。 由於關聯結構定義出兩變數間的結合關係，我們可利用關聯結構解釋兩事件發生時間之間的關係。假定兩變數之相關性參數為已知，且採用機率積分轉換的觀念，本論文討論了Zheng 與 Klein提出的關聯結構-圖形估計量，是否會依設限程度、相關性強度和關聯結構形式的不同，以致估計能力有別。 | zh_TW |
| dc.description.abstract (摘要) | The problem of estimating marginal distributions in a competing risks study is often met in scientific fields. Because main event and secondary event compete with each other, and a first occurring event prevents us from observing another event promptly, the intact lifetimes or survival times are unable to be collected in the circumstances that the probability of both lifetimes coinciding is 0. Unless lifetimes being independent or adding other conditions, there is a problem that the marginal distributions are non-identifiable. But the condition of independence is not always reasonable, we may assume the relation between lifetimes has some special form Because the copula defines the association between two variables, it can be employed to explain relation between lifetimes. Assuming that the dependence parameter in the copula framework is known, and adopting the concept of the probability integral transformations, this thesis has demonstrated whether the estimating abilities of the copula-graphic estimator, that Zheng and Klein put forward, are different in rates of censoring, intensities of dependence, and forms of the copula. | en_US |
| dc.description.tableofcontents | 第一章 緒論.............................................. 1 1.1 簡介................................................ 1 1.2 論文架構............................................ 4 第二章 關聯結構的介紹..................................... 5 2.1 阿基米德關聯結構的介紹................................ 8 2.2 事故傾向模式的介紹....................................13 2.3 相關性參數的介紹......................................16 2.4 估計相關性參數方法的介紹...............................18 第三章 相依競爭風險的介紹..................................21 3.1 無法識別的問題.......................................23 3.2 邊際分配的估計方法....................................24 第四章 模擬與分析.........................................30 第五章 結論與建議.........................................40 參考文獻........................................................41 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0094354011 | en_US |
| dc.subject (關鍵詞) | 競爭風險 | zh_TW |
| dc.subject (關鍵詞) | 無法識別 | zh_TW |
| dc.subject (關鍵詞) | 關聯結構 | zh_TW |
| dc.subject (關鍵詞) | 相關性參數 | zh_TW |
| dc.subject (關鍵詞) | 機率積分轉換 | zh_TW |
| dc.subject (關鍵詞) | 關聯結構-圖形估計量 | zh_TW |
| dc.subject (關鍵詞) | competing risks | en_US |
| dc.subject (關鍵詞) | non-identifiable | en_US |
| dc.subject (關鍵詞) | copula | en_US |
| dc.subject (關鍵詞) | dependence parameter | en_US |
| dc.subject (關鍵詞) | probability integral transformations | en_US |
| dc.subject (關鍵詞) | copula-graphic estimator | en_US |
| dc.title (題名) | 相依競爭風險邊際分配估計之探討 | zh_TW |
| dc.type (資料類型) | thesis | en |
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