The paper revisits the entropy-based efficiency of the type-I censored sample, which was addressed by several previous works. The main purpose of this work is to provide a comprehensive definition of the efficiency function and give a general proof that the entropy of a censored sample is always less than that of the complete sample for any probability distribution and at any point of censoring. A simulation study is performed to validate our results, and a real-data example is reevaluated.

中文翻译：

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关于 I 型删失样本的效率

本文重新审视了 I 型删失样本的基于熵的效率，这在之前的几项工作中得到了解决。这项工作的主要目的是提供效率函数的全面定义，并给出一般证明，即对于任何概率分布和任何删失点，删失样本的熵总是小于完整样本的熵。进行模拟研究以验证我们的结果，并重新评估真实数据示例。