理學院: 應用數學科學研究所指導教授: 江金倉陳世緯Chen, Shih-WeiShih-WeiChen2017-03-062018-06-282017-03-062018-06-282016http://ntur.lib.ntu.edu.tw//handle/246246/277740This article develops new approaches to estimate survival parameters based on two types of survival data without collecting survival times. The first one consists of incident and prevalent covariates and the other is a prevalent cohort sample with only covariates and truncation time. Our research aims to identify the effects of covariates on a failure time through more general single-index survival regression models. Under the assumption of covariate-independent truncation, the density ratio of incident and prevalent covariates and the hazard function of an observed truncation time are shown to be monotonic functions of the single-index in the proposed survival regression models. In light of these features, the rank correlation estimation technique can be naturally applied to estimate the index coefficients. Thus, existing theoretical frameworks can be used to establish the consistency and asymptotic normality of the proposed maximum rank correlation estimators. We further conduct a series of simulations to investigate the finite-sample performance of the estimators. In addition, our methodological ideas are illustrated by data from the National Comorbidity Survey Replicate.712062 bytesapplication/pdf論文公開時間: 2026/6/27論文使用權限: 同意有償授權(權利金給回饋學校)發生世代抽樣盛行世代抽樣排序相關估計式單指標存活模式incident cohort samplingprevalent cohort samplingrank correlation estimationsingle-index survival modelthesis10.6342/NTU201600523http://ntur.lib.ntu.edu.tw/bitstream/246246/277740/1/ntu-105-R03246005-1.pdf