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The fallacy of large shape parameters when using the two-parameter weibull distribution

机译:使用两参数韦伯分布时大形状参数的谬误

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摘要

The Weibull distribution is used to characterize the parameters of survival data. However, the two-parameter version of the Weibull model does not include a shift parameter. As the first breakages are sometimes occurring far from 0, the shape of the 2-parameter Weibull distribution must be extreme to accommodate such data. Here we show that Weibull distributions with extreme shapes are degenerate distributions which do not contain information about shape anymore. Because of this, any 2- parameter Weibull distribution with large shape parameter can be mimicked by another large-shaped distribution. The argument is further illustrated with simulated results. We present simple solutions to detect and avoid such situations.
机译:威布尔分布用于表征生存数据的参数。但是,威布尔模型的两参数版本不包含平移参数。由于第一个破损有时发生在远离0的位置,因此2参数Weibull分布的形状必须极端以容纳此类数据。在这里,我们表明具有极端形状的威布尔分布是退化分布,不再包含有关形状的信息。因此,任何具有大形状参数的2参数Weibull分布都可以被另一个大形状分布所模仿。用仿真结果进一步说明了该论点。我们提出了一些简单的解决方案来检测和避免这种情况。

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