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估计分布端点的贝叶斯似然方法AI 解读

Summary We consider maximum likelihood methods for estimating the end point of a distribution. The likelihood function is modified by a prior distribution that is imposed on the location parameter. The prior is explicit and meaningful, and has a general form that adapts itself to different settings. Results on convergence rates and limiting distributions are given. In particular, it is shown that the limiting distribution is non-normal in non-regular cases. Parametric…

Gumbel 吸引域中最大 k 个次序统计量的选择AI 解读

Statistical modeling of the largest k observations or the exceedances over a high threshold based on extreme value theory has produced powerful methods for drawing inferences about the tail behavior of a distribution; however, a method for selecting k has not been established. We suggest using a forward selection procedure based on a suitable goodness-of-fit statistic to search for the optimal k when the sample is of finite size and from a distribution in the domain of…

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