论文标题
用于多元混合分布的非参数估计的Prticle滤波器算法
A PRticle filter algorithm for nonparametric estimation of multivariate mixing distributions
论文作者
论文摘要
预测递归（PR）是一种快速的，递归的算法，在一般混合模型下对混合分布进行平滑估计。但是，PR算法需要评估每次迭代处的归一化常数。当混合分布的支持相对较低时，这不是问题，因为可以使用正交方法并且非常有效。但是，当支撑具有较高的维度时，正交方法效率低下，并且没有明显的基于蒙特卡洛的替代方案。在本文中，我们提出了一种新策略，我们将其称为prticle滤波器，其中我们通过使用过滤机制来增强基本PR算法，该机制可适应沿更新序列重新重新重量一组颗粒，该粒子用于获得蒙特卡洛的近似值。建立了Prticle滤波器近似的收敛性能，并通过模拟研究和明显的空间点过程数据分析来证明其经验精度。
Predictive recursion (PR) is a fast, recursive algorithm that gives a smooth estimate of the mixing distribution under the general mixture model. However, the PR algorithm requires evaluation of a normalizing constant at each iteration. When the support of the mixing distribution is of relatively low dimension, this is not a problem since quadrature methods can be used and are very efficient. But when the support is of higher dimension, quadrature methods are inefficient and there is no obvious Monte Carlo-based alternative. In this paper, we propose a new strategy, which we refer to as a PRticle filter, wherein we augment the basic PR algorithm with a filtering mechanism that adaptively reweights an initial set of particles along the updating sequence which are used to obtain Monte Carlo approximations of the normalizing constants. Convergence properties of the PRticle filter approximation are established and its empirical accuracy is demonstrated with simulation studies and a marked spatial point process data analysis.
