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厚尾与偏度的贝叶斯建模AI 解读

We consider a Bayesian analysis of linear regression models that can account for skewed error distributions with fat tails.The latter two features are often observed characteristics of empirical data sets, and we will formally incorporate them in the inferential process.A general procedure for introducing skewness into symmetric distributions is rst proposed.Even though this allows for a great deal of exibility in distributional shape, tail behaviour is not aected.In…

摘要暂缺

摘要暂缺

重访连续多元位置—尺度模型：稳健性研究AI 解读

SUMMARY We consider the location-scale family of all multivariate continuous sampling distributions. Any model from the subclass induced by fixing a particular distribution on the unit sphere, combined with a standard improper prior on the scale parameter, will lead to the same inference on location and observables. This finding sheds light on many robustness results in less general models and its implications are illustrated in a number of examples.