Combined Rank Tests for the Two-Sample Problem with Randomly Censored Data
随机删失两样本问题的组合秩检验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要聚焦通用删失数据的两样本秩检验，未明确涉及因果效应、分位数或其他所列细分。
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随机删失两样本问题的组合秩检验
- 研究问题
- 随机删失的两样本比较中，能否分别对删失与未删失观测构造秩统计量再组合，保持简便性及良好渐近性质？
- 方法与思路
- 作者分别计算两类观测的秩统计量并组合，通过限制删失分布为生存分布的适当函数，构造较简单的自适应评分函数。
- 材料陈述的贡献
- 摘要称该方法无需计算 Kaplan–Meier 估计，分开排序的损失渐近可忽略，并用精确效率计算与模拟考察限制性自适应。
- 阅读关注点
- 应核对随机删失条件、删失与生存分布的函数关系、评分函数选择，以及渐近损失结论和效率比较的适用范围。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；具体分布关系及效率结果未说明，不能认为限制性自适应对所有删失机制有效。
原文摘要
In the two-sample problem under random censorship we consider the uncensored observations and the censored ones as two separate groups. For each group a suitable rank statistic is obtained, and these two are then combined to a final one. This idea, which was first employed in Akritas (1983), is shown here to produce tests that (a) closely resemble the ordinary rank tests for the uncensored case and do not require the calculation of the Kaplan-Meier estimator, (b) are comparatively easy to apply and to understand, and (c) allow results on asymptotic normality to follow simply from standard results for the uncensored case. It is shown that the loss due to using two separate rankings rather than one complete ranking is asymptotically negligible. The optimal score function for each of the two separate rank statistics is seen to depend on the censoring distribution. Whereas in Akritas (1983) no assumption on the form of the censoring distribution was made (unrestricted adaptation), here we pursue restricted adaptation that results in simple score functions. In particular, motivated by the model of Koziol and Green (1976), we assume that the censoring distribution is a suitable function of the survival distribution. An example related to the Wilcoxon test is used for illustration. The effect of restricted adaptation on the efficiency of the test is examined by both exact efficiency calculations and simulation results.