Titlebook: Elliptically Symmetric Distributions in Signal Processing and Machine Learning; Jean-Pierre Delmas,Mohammed Nabil El Korso,Frédéri Book 20

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https://doi.org/10.1007/978-3-658-22209-3xible model allows for potentially diverse and independent samples that may not follow identical distributions. By deriving a new decision rule, we demonstrate that maximum-likelihood parameter estimation?and classification?are simple, efficient, and robust compared to state-of-the-art methods.

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FEMDA: A Unified Framework for?Discriminant Analysisxible model allows for potentially diverse and independent samples that may not follow identical distributions. By deriving a new decision rule, we demonstrate that maximum-likelihood parameter estimation?and classification?are simple, efficient, and robust compared to state-of-the-art methods.

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Fritz Aulinger,Wilm Reerink,Wolfgang Riepe the proposed algorithms are designed to handle various patterns of missing values. At the end of the chapter, the performances of the proposed procedures are illustrated on simulated datasets with missing values. We share a link to a code repository for fully reproducible experiments.

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Methodisches Erfinden im Personalmanagementnce matrix?(SSCM). The asymptotic distributions?of these estimators are also derived. This enables us to unify the asymptotic distribution?of subspace projectors?adapted to the different models of the data and demonstrate various invariance properties that have impacts on the parameters to be estima

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Elliptically Symmetric Distributions in Signal Processing and Machine Learning978-3-031-52116-4