A Smooth Nonparametric Estimate of a Mixing Distribution Using Mixtures of Gaussians
提出一种通过约束高斯混合成分方差不小于h的最大似然方法，来估计混合分布，光滑度由h控制，估计结果总是有限个方差为h的高斯混合，非参数最大似然估计是h=0的特例。
Abstract We propose a method of estimating mixing distributions using maximum likelihood over the class of arbitrary mixtures of Gaussians subject to the constraint that the component variances be greater than or equal to some minimum value h. This approach can lead to estimates of many shapes, with smoothness controlled by parameter h. We show that the resulting estimate will always be a finite mixture of Gaussians, each having variance h. The nonparametric maximum likelihood estimate can be viewed as a special case, with h = 0. The method can be extended to estimate multivariate mixing distributions. Examples and the results of a simulation study are presented.