论文标题
通过差异几何形状的惩罚可能性降低最大似然估计的渐近偏差
Asymptotic bias reduction of maximum likelihood estimates via penalized likelihoods with differential geometry
论文作者
论文摘要
开发了渐近偏置降低通用估计值的最大似然估计的程序。估计器被实现为插件估算器，其中参数以满足一阶的准线性偏微分方程的惩罚函数最大程度地提高了惩罚的可能性。讨论了借助差异几何形状的部分微分方程的整合。提出了广义线性模型，线性混合效应模型和位置尺度家族的应用。
A procedure for asymptotic bias reduction of maximum likelihood estimates of generic estimands is developed. The estimator is realized as a plug-in estimator, where the parameter maximizes the penalized likelihood with a penalty function that satisfies a quasi-linear partial differential equation of the first order. The integration of the partial differential equation with the aid of differential geometry is discussed. Applications to generalized linear models, linear mixed-effects models, and a location-scale family are presented.
