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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7165完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張淑惠 | |
| dc.contributor.author | Wan-Chu Lin | en |
| dc.contributor.author | 林莞筑 | zh_TW |
| dc.date.accessioned | 2021-05-19T17:39:56Z | - |
| dc.date.available | 2024-08-27 | |
| dc.date.available | 2021-05-19T17:39:56Z | - |
| dc.date.copyright | 2019-08-27 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-14 | |
| dc.identifier.citation | Brown, B. M., Wang, Y.G. (2005). Standard errors and covariance matrices for smoothed rank estimators. Biometrika 92, 149–158.
Clayton, D. G. (1978). A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65,141–151. Fu, T.C., Su, D.H., Chang, S.H. (2016) Serial association analyses of recurrent gap time data via Kendall's tau. Biostatistics 17, 188–202. Fuchs, H.J., Borowitz, D., Christiansen, D., Morris, E., Nash, M., Ramsey, B., Rosenstein, B.J., Smith,A.L. and Wohl, M.E. (1994). The effect of aerosilozed recombinant human DNase on respiratory exacerbations and pulmonary function in patients with cystic fibrosis. New England Journal of Medicine 331, 637—642 Jin, Z., Ying, Z., and Wei, L. J. (2001). A Simple Resampling Method by Perturbing the Minimand. Biometrika 88, 381–390. Lakhal-Chaieb, L., Cook, R. J. and Lin, X. (2010). Inverse probability of censoring weighted estimated of Kendall’s tau for gap time analyses. Biometrics 66, 1145–1152. Li, R., Cheng, Y., Chen, Q. and Fine, J. (2017). Quantile association for bivariate survival data. Biometrics, 73, 506–516. Lin, D. Y. and Ying, Z. (1993). A simple nonparametric estimator of the bivariate survival function under univariate censoring. Biometrika 80, 573–581. Nan, B., Lin, X., Lisabeth, L. D., and Harlow, S. D. (2006). Piecewise constant cross-ratio estimation for association of age at a marker event and age at menopause. Journal of the American Statistical Association 101, 65–77. Pang, L., Lu, W., and Wang, H. (2012). Variance Estimation in Censored Quantile Regression via Induced Smoothing. Computational Statistics & Data Analysis 56, 785–796. Peng, L., Fine, J. (2009). Competing risks quantile regression. Journal of the American Statistical Association 104, 1440–1453. Peng, L., and Huang, J. (2008). Survival Analysis with Quantile Regression Models. Journal of the American Statistical Association 103, 637-649. Yang, J., Peng, L., (2016). A new flexible dependence measure for semi-competing risks. Biometrics 72, 770– 779 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7165 | - |
| dc.description.abstract | 序列事件常見於現代醫學和流行病學縱向研究中，而有序間隔時間資料常為研究者感興趣之主題。研究者欲了解病人的疾病進程而進行早期預防治療，而有序間隔時間之間相關性蘊含病人疾病歷程資訊，是自然的預測因子。本篇提出交叉分位數比 (cross quantile ratio，CQR) 測量隨時間改變之相依性，並以前兩段間隔時間為例。利用無母數方法，設限分位數迴歸估計交叉分位數比以及使用設限機率倒數權重調整相依設限，而不須額外假設聯合分布。本文另外討論交叉分位數比估計之一致性以及大樣本下共變異數估計。模擬結果顯示，交叉分位數比表現受限於設限率，在一定範圍的分位數相對偏誤都在5%以下；本文提供之標準差估計方法在大樣本下會有較好表現，最後以rhDNase資料為例，進行實際資料分析。 | zh_TW |
| dc.description.abstract | Serial event data are often encountered and of interest in the follow-up studies of chronic diseases and gap times between successive events. The relationship between serial gap times may provide predictive information on disease progression. In this thesis, the cross quantile ratio (CQR) is introduced to measure the time-varying dependence between the first and second gap times without specifying their joint distribution. Nonparametric estimation of cross quantile ratio can be carried out through censored quantile regression approach. In addition, the inverse probability of censoring weights is used to tackle the induced dependent censoring. The asymptotic properties of the proposed estimators are investigated and the corresponding asymptotic variance estimators are provided as well. Simulation results suggest good performance of the proposed methods within a certain range of quantile due to censoring. The rhDNase dataset is analyzed for further illustration of CQR method | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:39:56Z (GMT). No. of bitstreams: 1 ntu-108-R05849013-1.pdf: 2413649 bytes, checksum: 1ba23468a07c05e6bba1ac550d25cc45 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 目錄
誌謝 I 摘要 II Abstract III 第一章 導論 1 第一節 前言 1 第二節 研究動機 1 第二章 文獻回顧 3 第一節 分位數迴歸模型於存活資料 3 第二節 交叉殘差分位數比(非單次事件) 4 第三節 分位數勝算比 7 第四節 使用誘導平滑方法估計分位數迴歸模型的變異數 9 第三章 方法 11 第一節 交叉分位數比 12 第二節 有序間隔時間交叉分位數比估計 14 第三節 交叉分位數比估計量之共變異數估計 16 第四章 模擬 20 第一節 阿基米德耦合 20 第二節 對數常態模型 25 第三節 分段常數克萊頓 (Piecewise constant Clayton) 28 第五章 實際資料分析 32 第一節 資料探索 34 第二節 納入過去歷史 36 第六章 討論與總結 41 第七章 參考文獻 43 附錄 45 A. 理論A之證明 (consistency) 45 B. 理論B之證明 (asymptotic normal) 47 C. 共變異數之估計步驟 51 D. Resampling之理論證明 54 E. 模擬結果表格 55 | |
| dc.language.iso | zh-TW | |
| dc.title | 二元有序間隔時間之交叉分位數比分析 | zh_TW |
| dc.title | Analysis of cross quantile ratio for two serial gap times | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 戴政,丘政民,陳秀熙 | |
| dc.subject.keyword | 間隔時間,相關性,交叉分位數比,分位數迴歸, | zh_TW |
| dc.subject.keyword | gap times,association,cross quantile ratio,quantile regression, | en |
| dc.relation.page | 70 | |
| dc.identifier.doi | 10.6342/NTU201704452 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2019-08-15 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| dc.date.embargo-lift | 2024-08-27 | - |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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| ntu-108-1.pdf | 2.36 MB | Adobe PDF | 檢視/開啟 |
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