Nonparametric Estimation and Tests for Serial Event Data with Univariate Monitoring Times
Date Issued
2007
Date
2007
Author(s)
Lin, Zong-Ying
DOI
zh-TW
Abstract
Serial event data arise when a series of events occur orderly. Taking the diabetes course as an example, the progression of the disease is necessarily from health to diabetes and subsequently to diabetic complication. Because of the ordinal characteristic of serial events, the current status of two serial events can be observed simultaneously under univariate monitoring. Specifically, two serial event data under univariate monitoring can be regarded as two univarate current status data sets. First, we can obtain nonparametric estimation of the marginal survival function of time to each serial event by using the nonparametric estimation method developed for the current status data. In addition, for comparison of two survival functions based on current status data with the same monitoring time distribution, we proposed a Wilcoxon-type test which is generalized from Gehan test. Secondly, for comparison of the difference between two processes with two serial events under univariate monitoring, we developed two robust nonparametric tests which are extended from our proposed Wilcoxon-type test and the residual-based test proposed by Sun and Kalbfleisch (1993) for univariate current status data with the same monitoring time distribution. Specifically, our proposed methods do not specify the correlation structure of two serial events. Finally, we compare the performance of two proposed nonparametric tests by simulations and illustrate them with a real data.
Subjects
現時狀態資料
無母數估計
無母數檢定
穩健性
序列事件資料
current status data
nonparametric estimation
nonparametric test
robustness
serial event data
SDGs
Type
thesis
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