估计理论
数学
估计员
高斯分布
多元统计
参数统计
应用数学
形状参数
最大似然序列估计
算法
多元正态分布
散射矩阵
统计
量子力学
物理
作者
Frédéric Pascal,Lionel Bombrun,Jean‐Yves Tourneret,Yannick Berthoumieu
标识
DOI:10.1109/tsp.2013.2282909
摘要
Due to its heavy-tailed and fully parametric form, the multivariate generalized Gaussian distribution (MGGD) has been receiving much attention in signal and image processing applications. Considering the estimation issue of the MGGD parameters, the main contribution of this paper is to prove that the maximum likelihood estimator (MLE) of the scatter matrix exists and is unique up to a scalar factor, for a given shape parameter β ∈ (0,1). Moreover, an estimation algorithm based on a Newton-Raphson recursion is proposed for computing the MLE of MGGD parameters. Various experiments conducted on synthetic and real data are presented to illustrate the theoretical derivations in terms of number of iterations and number of samples for different values of the shape parameter. The main conclusion of this work is that the parameters of MGGDs can be estimated using the maximum likelihood principle with good performance.
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