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有限混合模型的最小 Hellinger 距离估计AI 解读

Minimum Hellinger distance estimates are considered for finite mixture models when the exact forms of the component densities are unknown in detail but are thought to be close to members of some parametric family. Minimum Hellinger distance estimates are asymptotically efficient if the data come from a member of the parametric family and are robust to certain departures from the parametric family. A new algorithm is introduced that is similar to the EM algorithm a…

有限混合模型的最小 Hellinger 距离估计AI 解读

Minimum Hellinger distance estimates are considered for finite mixture models when the exact forms of the component densities are unknown in detail but are thought to be close to members of some parametric family. Minimum Hellinger distance estimates are asymptotically efficient if the data come from a member of the parametric family and are robust to certain departures from the parametric family. A new algorithm is introduced that is similar to the EM algorithm a…

用于验证混合分布分析的信息比率AI 解读

Determining the number of components in a mixture of distributions is an important but difficult problem. This article introduces a procedure called minimum information ratio estimation and validation (MIREV), which is based on a ratio of Fisher information matrices. The smallest eigenvalue of the information ratio matrix is used to determine the number of components. A measure of uncertainty may be obtained using a bootstrap technique. Simulations illustrate the…