论文标题
与指数功率分布的强大混合物回归
Robust mixture regression with Exponential Power distribution
论文作者
论文摘要
假设指数式发电分布是应对回归和聚类异常值的一种方法，这可以提高分析的鲁棒性。高斯分布是指数分布的特殊情况。并且指数式发电分布可以看作是正常分布的比例混合物。因此，为高斯混合物模型开发的模型选择方法可以轻松扩展到指数功率混合物模型。此外，在现实情况下，高斯混合模型倾向于选择比指数电源混合模型更多的组件，这意味着指数的电力混合物模型更易于解释。在本文中，当假定误差遵循指数功率分布时，我们对混合回归模型进行了分析。这对于离群值将是可靠的，并且模型选择易于实现。
Assuming an exponential power distribution is one way to deal with outliers in regression and clustering, which can increase the robustness of the analysis. Gaussian distribution is a special case of an exponential distribution. And an exponential power distribution can be viewed as a scale mixture of normal distributions. Thus, model selection methods developed for the Gaussian mixture model can be easily extended for the exponential power mixture model. Moreover, Gaussian mixture models tend to select more components than exponential power mixture models in real-world cases, which means exponential power mixture models are easier to interpret. In this paper, We develop analyses for mixture regression models when the errors are assumed to follow an exponential power distribution. It will be robust to outliers, and model selection for it is easy to implement.
