We show that, for a wide range of models, the empirical velocity of processive motor proteins has a limiting Pearson type VII distribution with finite mean but infinite variance. We develop maximum likelihood inference for this Pearson type VII distribution. In two simulation studies, we compare the performance of our MLE with the performance of standard Student's t-based inference. The studies show that incorrectly assuming normality (1) can lead to imprecise inference regarding motor velocity in the one-sample case, and (2) can significantly reduce power in the two-sample case. These results should be of interest to experimentalists who wish to engineer motors possessing specific functional characteristics.

中文翻译：

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用随机分离估计进行性运动蛋白的速度。

我们表明，对于广泛的模型，进行性运动蛋白的经验速度具有有限的 Pearson VII 型分布，具有有限的均值但无限的方差。我们为这个 Pearson VII 型分布开发了最大似然推理。在两个模拟研究中，我们将 MLE 的性能与标准学生的基于 t 的推理的性能进行了比较。研究表明，错误地假设正态性 (1) 会导致在单样本情况下对电机速度的推断不准确，并且 (2) 会在双样本情况下显着降低功率。希望设计具有特定功能特性的电机的实验者应该会对这些结果感兴趣。