论文标题
多变量正常混合模型的最大伪\ b {eta} - likelihood估计器的存在和一致性
Existence and Consistency of the Maximum Pseudo \b{eta}-Likelihood Estimators for Multivariate Normal Mixture Models
论文作者
论文摘要
多元正常（MVN）混合模型下的强大估计始终是计算挑战。最近提出的最大伪\ b {eta} -likelihood估计量旨在以最小密度差异（DPD）方法的精神估算MVN混合模型的未知参数，但具有相对简单，更易于的计算算法的较大尺寸。在这封信中，在合理的假设集中，如果MVN混合模型在MVN混合模型的情况下，我们将严格地得出最大伪\ b {eta} -likelihood估计器的存在和弱一致性。
Robust estimation under multivariate normal (MVN) mixture model is always a computational challenge. A recently proposed maximum pseudo \b{eta}-likelihood estimator aims to estimate the unknown parameters of a MVN mixture model in the spirit of minimum density power divergence (DPD) methodology but with a relatively simpler and tractable computational algorithm even for larger dimensions. In this letter, we will rigorously derive the existence and weak consistency of the maximum pseudo \b{eta}-likelihood estimator in case of MVN mixture models under a reasonable set of assumptions.
