论文标题
可伸缩的蒙特卡洛推断和重新缩放的局部渐近正态性
Scalable Monte Carlo Inference and Rescaled Local Asymptotic Normality
论文作者
论文摘要
在本文中，我们以重新缩放的局部渐近正态性（RLAN）的名义将局部渐近正态性（LAN）的财产推广到扩大的社区。我们获得了足够的条件，以使常规参数模型满足RLAN。我们表明，RLAN支持构造统计高效的估计器，该估计量最大化了该扩大邻域对数模型的立方近似。在蒙特卡洛推论的背景下，我们发现这种最大的立方可能性估计量可以在渐近地增加蒙特卡洛误差的可能性评估中保持其统计效率。
In this paper, we generalize the property of local asymptotic normality (LAN) to an enlarged neighborhood, under the name of rescaled local asymptotic normality (RLAN). We obtain sufficient conditions for a regular parametric model to satisfy RLAN. We show that RLAN supports the construction of a statistically efficient estimator which maximizes a cubic approximation to the log-likelihood on this enlarged neighborhood. In the context of Monte Carlo inference, we find that this maximum cubic likelihood estimator can maintain its statistical efficiency in the presence of asymptotically increasing Monte Carlo error in likelihood evaluation.
