Mixtures of regression models with incomplete and noisy data (Q4563423)

scientific article; zbMATH DE number 6879737

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English	Mixtures of regression models with incomplete and noisy data	scientific article; zbMATH DE number 6879737

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instance of

scholarly article

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title

Mixtures of regression models with incomplete and noisy data (English)

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1 June 2018

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zbMATH Keywords

EM algorithm

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maximum likelihood

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missing values

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mixtures of regression models

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outliers

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describes a project that uses

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full work available at URL

https://doi.org/10.1080/03610918.2017.1283700

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cites work

Missing data

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Robust fitting of mixture regression models

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Robust Mixture of Linear Regression Models

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Model-Based Gaussian and Non-Gaussian Clustering

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Maximum likelihood estimation of heterogeneous mixtures of Gaussian and uniform distributions

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Trimmed \(k\)-means: An attempt to robustify quantizers

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Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering

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Q4139463

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A maximum likelihood methodology for clusterwise linear regression

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Model-Based Clustering, Discriminant Analysis, and Density Estimation

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Finite mixture and Markov switching models.

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A mixture of experts model for rank data with applications in election studies

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Fitting finite mixtures of generalized linear regressions in \textsf{R}

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Q3723487

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Identifiability of models for clusterwise linear regression

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Breakdown points for maximum likelihood estimators of location-scale mixtures

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Fitting a mixture model to three-mode three-way data with categorical and continuous variables

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Mixture model clustering for mixed data with missing information

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Local statistical modeling via a cluster-weighted approach with elliptical distributions

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Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation

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Q3535289

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On fast supervised learning for normal mixture models with missing information

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Supervised learning of multivariate skew normal mixture models with missing information

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Q4779059

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Flexible mixture modeling via the multivariate \(t\) distribution with the Box-Cox transformation: an alternative to the skew-\(t\) distribution

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Finite mixture models

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Robust joint modeling of mean and dispersion through trimming

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Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Application to Speech Recognition

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A New Approach to Estimating Switching Regressions

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Inference and missing data

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Robust clustering by deterministic agglomeration EM of mixtures of multivariate \(t\)-distributions

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Robust mixture regression model fitting by Laplace distribution

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A mixture likelihood approach for generalized linear models

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Robust mixture regression using the \(t\)-distribution

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Imputation through finite Gaussian mixture models

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published in

Communications in Statistics. Simulation and Computation

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Identifiers

zbMATH Open document ID

1392.62154

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DOI

10.1080/03610918.2017.1283700

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:4563423