论文标题
从分组观测值对可识别的非参数混合模型的一致估计
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
论文作者
论文摘要
最近的研究已经建立了足够的条件，可以从分组的观察结果中识别有限混合模型。这些条件允许混合组件非参数，并且具有实质性（甚至是总）重叠。这项工作提出了一种算法，该算法始终从分组观测值中估算任何可识别的混合模型。我们的分析利用了甲骨文的不等式，以使组分布的加权核密度估计器以及一般结果表明，对组上的分布的一致估计意味着对混合成分的一致估计。为配对的观测值提供了实际实现，该方法显示出优于现有方法，尤其是当混合组件显着重叠时。
Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations. These conditions allow the mixture components to be nonparametric and have substantial (or even total) overlap. This work proposes an algorithm that consistently estimates any identifiable mixture model from grouped observations. Our analysis leverages an oracle inequality for weighted kernel density estimators of the distribution on groups, together with a general result showing that consistent estimation of the distribution on groups implies consistent estimation of mixture components. A practical implementation is provided for paired observations, and the approach is shown to outperform existing methods, especially when mixture components overlap significantly.
