The exponentiated Weibull distribution is a convenient alternative to the generalized gamma distribution to model time‐to‐event data. It accommodates both monotone and nonmonotone hazard shapes, and flexible enough to describe data with wide ranging characteristics. It can also be used for regression analysis of time‐to‐event data. The maximum likelihood method is thus far the most widely used technique for inference, though there is a considerable body of research of improving the maximum likelihood estimators in terms of asymptotic efficiency. For example, there has recently been considerable attention on applying James–Stein shrinkage ideas to parameter estimation in regression models. We propose nonpenalty shrinkage estimation for the exponentiated Weibull regression model for time‐to‐event data. Comparative studies suggest that the shrinkage estimators outperform the maximum likelihood estimators in terms of statistical efficiency. Overall, the shrinkage method leads to more accurate statistical inference, a fundamental and desirable component of statistical theory.

中文翻译：

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时间到事件数据的指数Weibull回归模型的收缩估计

指数化的Weibull分布是对时间事件数据建模的广义伽马分布的便捷替代方法。它可以容纳单调和非单调危险形状，并且足够灵活以描述具有广泛特征的数据。它也可以用于事件时间数据的回归分析。迄今为止，最大似然法是最广泛使用的推理技术，尽管在渐近效率方面有大量研究可以改进最大似然估计器。例如，最近在将James–Stein收缩思想应用于回归模型中的参数估计方面引起了相当大的关注。我们为事件数据的指数化Weibull回归模型提出了非惩罚收缩估计。比较研究表明，就统计效率而言，收缩估计量优于最大似然估计量。总体而言，收缩方法可导致更准确的统计推断，这是统计理论的基本和理想组成部分。