| dc.contributor.advisor | 陳麗霞 | zh_TW |
| dc.contributor.author (作者) | 陳嬿婷 | zh_TW |
| dc.creator (作者) | 陳嬿婷 | zh_TW |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 9-五月-2016 15:11:35 (UTC+8) | - |
| dc.date.available | 9-五月-2016 15:11:35 (UTC+8) | - |
| dc.date.issued (上傳時間) | 9-五月-2016 15:11:35 (UTC+8) | - |
| dc.identifier (其他 識別碼) | G0096354022 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/95122 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計學系 | zh_TW |
| dc.description (描述) | 96354022 | zh_TW |
| dc.description.abstract (摘要) | 存活分析主要在研究事件的發生時間；傳統的存活分析並不考慮治癒者(或免疫者)的存在。若以失敗為事件，且造成失敗的可能原因不止一種，但它們不會同時發生，則這些失敗原因就是失敗事件的競爭風險。競爭風險可分為有參數的競爭風險與無母數的競爭風險。本文同時考慮了有治癒與有參數的混合廣義Gamma分配，並將預估計的位置參數與失敗機率有關的參數與解釋變數結合，代入Choi及Zhou(2002)提出的最大概似估計量的大樣本性質。並考慮在治癒情況下，利用電腦模擬來估計在型一設限及無訊息(non-informative)的隨機設限(random censoring)下之一個失敗原因與兩個失敗原因下的參數平均數與標準差。 | zh_TW |
| dc.description.abstract (摘要) | The purpose of survival analysis is aiming to analyze the timeline of events. The typically method of survival analysis don’t take account of the curer (or the immune). If the event is related to failure and there are more than one possible reason causing the failure but are not happening at the same time, we called the possible reasons a competing risk for failed occurrence. competing risk can be categorized as parameter and non-parameter. This research has considered the generalized gamma distribution over both cure and parameter aspects. In addition, it combines anticipated parameter with covariate which affected to the possibilities of failure. Follow by the previous data, it is then substituted by the large-sample property of the maximum likelihood estimator which is presented by Choi and Zhou in 2002. With considering the possibilities of cure, it uses computer modeling to investigate that under the condition of type-1 censoring and non-informatively random censoring, we will find out the parameter mean and standard error that is resulted by one and two reason causes failure. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1 1-1節 介紹 1 1-2節 文獻回顧 2 1-3節 論文架構 3 第二章 參數混合模型在競爭風險上的分析 4 2-1節 在競爭風險資料下的參數混合模型 4 2-2節 以廣義對數Gamma分配為例之參數估計 6 2-3節 參數檢定與區間估計 13 第三章 模擬與計算 20 3-1節 牛頓法 20 3-2節 模擬研究 22 第四章 結論與建議 37 參考文獻 38 | zh_TW |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0096354022 | en_US |
| dc.subject (關鍵詞) | 競爭風險 | zh_TW |
| dc.subject (關鍵詞) | 廣義Gamma分配 | zh_TW |
| dc.subject (關鍵詞) | Competing Risk | en_US |
| dc.subject (關鍵詞) | Generalized Gamma Distribution | en_US |
| dc.title (題名) | 廣義Gamma分配在競爭風險上的分析 | zh_TW |
| dc.title (題名) | An analysis on generalized Gamma distribution`s application on competing risk | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1. Allgower, E.L. and Georg, K. (1990). Numerical Continuation Methods. Berlin: Springer-Verlag. 2. Bartlett, M.S. and Kendall, D.G. (1946).The statistical analysis of variance-heterogeneity and the logarithmic transformation. Journal of the Royal Statistical Society 8, 128-138. 3. Bader, B.W.(2005).Tensor–Krylov methods for solving large-scale systems of nonlinear equations. SIAM Journal of Numerical Analysis 43, 1321–1347. 4. Berkson, J. and Elveback, L. (1960). Competing exponential risks with particular inference to the study of smoking lung cancer. Journal of the American Statistical Association 55, 415-428. 5. Berkson, J. and Gage, P.R.(1952).Survival curve for cancer patients following treatment. Journal of the American Statistical Association 47, 501-515. 6. Boag, J. W.(1949). Maximum likelihood estimates of the proportion of patients cured by caner therapy. Journal of the Royal statistical Society B 11, 15-45. 7. Choi, K.C. and Zhou, X.(2002). Large-sample properties of mixture models with covariates for competing risks. Journal of Multivariate Analysis 82, 331-366. 8. Cox, D. R. (1972). Regression Models and Life Tables (with Discussion). Journal of the Royal Statistical Society Series B 34, 187-220. 9. Farewell, V. T. (1982). The use of mixture models for the analysis of survival data with long-term survivors. Biometrics 38, 1041-1046. 10. Haybittle, J.L. (1965). A two-parameter model for the survival curve of treated cancer patients. Journal of the American Statistical Association 53, 16-26. 11. Kuk, A. Y. C. and Chen, C. (1992). A mixture model combining logistic regression with proportional hazards regressions. Biometrika 79, 531-541. 12. Larson M.G. and Dinse G.E. (1985). A mixture model for the regression analysis of competing risks data. Applied Statistics 34, 201-211. 13. Lawless, J.F. (1980). Inference in the generalized gamma and log gamma distributions. Technometrics 22, 409-419. 14. Maller, R.A. and Zhou, X. (2002).Analysis of parametric models for competing risks. Statistica Sinica 2, 725-750. 15. Stay, E.W.(1962). A generalization of the gamma distribution. Annals of Mathematical Statistics 33, 1187-1192. 16. Taylor, J. M. G. (1995). Semi-parametric estimation in failure time mixture models. Biometrics 51, 899-907. | zh_TW |
