The suppport reduction algorithm for computing nonparametric function estimates in mixture models
The suppport reduction algorithm for computing nonparametric function estimates in mixture models
复制标题
用于计算混合模型中非参数函数估计的支持减少算法
DOI：
--
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Jon A. Wellner
中科院分区：
文献类型：
--
作者：
P. Groeneboom;G. Jongbloed;Jon A. Wellner
顶点方向算法在实验设计和混合模型文献中已经存在了几十年。我们简要回顾一下这种类型的算法，并描述该算法家族的一个新成员：支持度缩减算法。支持减少算法应用于计算两个反问题中的非参数估计的问题：凸密度估计和高斯反卷积问题。通常，VD 算法解决感兴趣的有限维（版本）优化问题。我们介绍一种解决真正的无限维优化问题的方法。
Vertex direction algorithms have been around for a few decades in the experimental design and mixture models literature. We briefly review this type of algorithm and describe a new member of the family: the support reduction algorithm. The support reduction algorithm is applied to the problem of computing nonparametric estimates in two inverse problems: convex density estimation and the Gaussian deconvolution problem. Usually, VD algorithms solve a finite dimensional (version of the) optimization problem of interest. We introduce a method to solve the true infinite dimensional optimization problem.