Maximum likelihood estimation of mixture densities for binned and truncated multivariate data

DOI10.1023/A:1013679611503zbMath1012.68057OpenAlexW2156655440MaRDI QIDQ5959975

Christine E. McLaren, Padhraic Smyth, Igor V. Cadez, Geoff J. McLachlan

Publication date: 11 April 2002

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1013679611503

zbMATH Keywords

data analysismachine learningbinningtruncation of data

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Data structures (68P05)

Related Items (11)

EM algorithms for multivariate Gaussian mixture models with truncated and censored data ⋮ Degeneracy in the Maximum Likelihood Estimation of Univariate Gaussian Mixtures for Grouped Data and Behaviour of the EM Algorithm ⋮ Toward an Efficient Computation of Log-Likelihood Functions in Statistical Inference: Overdispersed Count Data Clustering ⋮ Frugal Gaussian clustering of huge imbalanced datasets through a bin-marginal approach ⋮ Distance-based variable generation with applications to the FACT experiment ⋮ Power-law distributions in binned empirical data ⋮ Allowing for the effect of data binning in a Bayesian normal mixture model ⋮ Bayesian approach for mixture models with grouped data ⋮ A classification EM algorithm for binned data ⋮ Inference of finite mixture models and the effect of binning ⋮ EM algorithm for mixture of skew-normal distributions fitted to grouped data

Uses Software

• AS 254

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