Deprecated: $wgMWOAuthSharedUserIDs=false is deprecated, set $wgMWOAuthSharedUserIDs=true, $wgMWOAuthSharedUserSource='local' instead [Called from MediaWiki\HookContainer\HookContainer::run in /var/www/html/w/includes/HookContainer/HookContainer.php at line 135] in /var/www/html/w/includes/Debug/MWDebug.php on line 372
Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods - MaRDI portal

Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods

From MaRDI portal
Publication:3391121

DOI10.1080/10618600.2017.1390469OpenAlexW2505884832MaRDI QIDQ3391121

Marco Riani, Andrea Cerioli, Agustín Mayo-Iscar, Luis Angel García-Escudero

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: http://uvadoc.uva.es/handle/10324/32023




Related Items (20)

Selection of the number of clusters in functional data analysisRobust fitting of mixture models using weighted complete estimating equationsRobust model-based clustering with mild and gross outliersRobust clustering for functional data based on trimming and constraintsAssessing trimming methodologies for clustering linear regression dataEigenvalues and constraints in mixture modeling: geometric and computational issuesAn adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture‐based clusteringRobust inference for parsimonious model-based clusteringOver-optimistic evaluation and reporting of novel cluster algorithms: an illustrative studyGraphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust ModelA robust approach to model-based classification based on trimming and constraints. Semi-supervised learning in presence of outliers and label noiseModel-based clustering with determinant-and-shape constraintDetecting Wine Adulterations Employing Robust Mixture of Factor AnalyzersRobust fuzzy clustering of time series based on B-splinesAnomaly and novelty detection for robust semi-supervised learningConstrained parsimonious model-based clusteringExploration of the variability of variable selection based on distances between bootstrap sample resultsWeighted likelihood latent class linear regressionSemiautomatic robust regression clustering of international trade dataComments on ``The power of monitoring: how to make the most of a contaminated multivariate sample


Uses Software


Cites Work


This page was built for publication: Finding the Number of Normal Groups in Model-Based Clustering via Constrained Likelihoods