A Recursive Algorithm for Mixture of Densities Estimation

Sancetta; A.

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A Recursive Algorithm for Mixture of Densities Estimation

机译：混合密度估计的递归算法

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摘要

Recursive algorithms for the estimation of mixtures of densities have attracted a lot of attention in the last 10 years. Here an algorithm for recursive estimation is studied. It complements existing approaches in the literature, as it is based on conditions that are usually very weak. For example, the parameter space over which the mixture is taken does not need to be necessarily bounded. The essence of the procedure is to combine density estimation via empirical characteristic function together with an iterative Hilbert space approximation algorithm. The conditions for consistency of the estimator are verified for three important statistical problems. A simulation study is also included.

机译：在过去的十年中，用于估计密度混合的递归算法引起了很多关注。这里研究了一种递归估计算法。它是对文献中现有方法的补充，因为它基于通常非常薄弱的条件。例如，不需要在其上获取混合物的参数空间是有界的。该程序的本质是将通过经验特征函数进行的密度估计与迭代的希尔伯特空间逼近算法结合在一起。针对三个重要的统计问题，验证了估计量一致性的条件。模拟研究也包括在内。

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《IEEE Transactions on Information Theory》 |2013年第10期|6893-6906|共14页
作者
Sancetta; A.;
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作者单位

Department of Economics, Royal Holloway, Egham Hill, U.K.|c|;

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原文格式 PDF
正文语种 eng
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关键词
Boosting; Hilbert space; copula; elliptic distribution; empirical characteristic function; location model; recursive algorithm;

机译：Boosting;希尔伯特空间;copula;椭圆分布;经验特征函数;位置模型;递归算法;

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A Recursive Algorithm for Mixture of Densities Estimation

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