Regression Analysis of Doubly Truncated Data
双重截断数据的回归分析AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要提出通用双重截断回归方法，提及计量应用但未明确落入所列具体领域方向。
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双重截断数据的回归分析
- 研究问题
- 当因变量仅在各自允许区间内才被观测时，如何估计回归参数，并为双重截断所造成的样本选择开展有效推断？
- 方法与思路
- 作者扩展 Mann–Whitney 型秩估计，构造一般回归参数估计方法，同时考虑加权秩估计和近似极限分布的重抽样方案。
- 材料陈述的贡献
- 摘要称估计量具有一致性和渐近正态性，重抽样获得大样本依据，并以类星体与艾滋病潜伏期数据及模拟展示方法。
- 阅读关注点
- 应核对截断区间与结果变量的关系要求、回归模型形式、权重选择依据，以及重抽样如何保留双重截断的观测机制。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；摘要未说明可识别性条件、效率改善幅度或重抽样细节，不能推断刊本变化。
原文摘要
Doubly truncated data are found in astronomy, econometrics and survival analysis literature. They arise when each observation is confined to an interval, i.e., only those which fall within their respective intervals are observed along with the intervals. Unlike the one-sided truncation that can be handled by counting process-based approach, doubly truncated data are much more difficult to handle. In their analysis of an astronomical data set, Efron and Petrosian (1999) proposed some nonparametric methods for doubly truncated data. Motivated by their approach, as well as by the work of Bhattacharya et al. (1983) for right truncated data, we propose a general method for estimating the regression parameter when the dependent variable is subject to the double truncation. It extends the Mann-Whitney-type rank estimator and can be computed easily by existing software packages. Weighted rank estimation are also considered for improving estimation efficiency. We show that the resulting estimators are consistent and asymptotically normal. Resampling schemes are proposed with large sample justification for approximating the limiting distributions. The quasar data in Efron and Petrosian (1999) and an AIDS incubation data are analyzed by the new method. Simulation results show that the proposed method works well.