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• W2022971702 abstract "A framework of using t mixture models with fourteen eigen-decomposed covariance structures for the unsupervised learning of heterogeneous multivariate data with possible missing values is designed and implemented. Computationally flexible EM-type algorithms are developed for parameter estimation of these models under a missing at random (MAR) mechanism. For ease of computation and theoretical developments, two auxiliary indicator matrices are incorporated into the estimating procedure for exactly extracting the location of observed and missing components of each observation. Computational aspects related to the specification of starting values, convergence assessment and model choice are also discussed. The practical usefulness of the proposed methodology is illustrated with real data examples and a simulation study with varying proportions of missing values." @default.
• W2022971702 created "2016-06-24" @default.
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• W2022971702 date "2014-03-01" @default.
• W2022971702 modified "2023-09-27" @default.
• W2022971702 title "Learning from incomplete data via parameterized <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML altimg=si29.gif display=inline overflow=scroll><mml:mi>t</mml:mi></mml:math> mixture models through eigenvalue decomposition" @default.
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• W2022971702 doi "https://doi.org/10.1016/j.csda.2013.02.020" @default.
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