The paper presents a novel asymptotic distribution for a mle when the log--likelihood is strictly concave in the parameter for all data points; for example, the exponential family. The new asymptotic distribution can be seen as a refinement of the usual normal asymptotic distribution and is comparable to an Edgeworth expansion. However, it is obtained with weaker conditions than even those for asymptotic normality. The same technique is then used to find the exact distribution of the weighted likelihood bootstrap sampler.
翻译:当日志相似性在所有数据点的参数(例如指数式组)中都完全混为一谈时,本文为一毫尔提供了一种新的无症状分布。新的无症状分布可视为对通常正常的无症状分布的改进,可与Edgeworth的扩展相比。然而,在获得该分布时的条件比对无症状正常性的条件要差。然后使用同样的技术来寻找加权可能性靴式采样器的确切分布。