標題: Estimating the association parameter for copula models under dependent censoring
作者: Wang, WJ
統計學研究所
Institute of Statistics
關鍵字: archimedean copula models;bivariate survival analysis;competing risk;cross-ratio function;estimating function;frailty models;identifiability;Kendall's tau;log-rank statistic;multistate process;semi-competing-risks data;semi parametric inference
公開日期: 2003
摘要: Many biomedical studies involve the analysis of multiple events. The dependence between the times to these end points is often of scientific interest. We investigate a situation when one end point is subject to censoring by the other. The model assumptions of Day and co-workers and Fine and co-workers are extended to more general structures where the level of association may vary with time. Two types of estimating function are proposed. Asymptotic properties of the proposed estimators are derived. Their finite sample performance is studied via simulations. The inference procedures are applied to two real data sets for illustration.
URI: http://hdl.handle.net/11536/28172
http://dx.doi.org/10.1111/1467-9868.00385
ISSN: 1369-7412
DOI: 10.1111/1467-9868.00385
期刊: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
Volume: 65
起始頁: 257
結束頁: 273
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