An algorithmic approach to identification of gray areas: analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model
type: Item
instance of: scholarly article
title: An algorithmic approach to identification of gray areas: analysis of sleep scoring expert ensemble non agreement areas using a multinomial mixture model
author: Gabriel Jouan
author: Erna Sif Arnardottir
author: Anna Sigridur Islind
author: María Óskarsdóttir
published in: European Journal of Operational Research
publication date: 2024-07-16T00:00:00Z
Mathematics Subject Classification ID: 90Bxx
zbMATH DE Number: 7881393
zbMATH Keywords: ensemble of manual scorers
zbMATH Keywords: multinomial mixture model
zbMATH Keywords: sleep stages
zbMATH Keywords: uncertainty quantification
zbMATH Keywords: gray area
MaRDI profile type: Publication
cites work: Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data
cites work: A hierarchical ant based clustering algorithm and its use in three real-world applications
cites work: EM for mixtures
cites work: A Lasso-penalized BIC for mixture model selection
cites work: Q5242721
cites work: Funzione caratteristica di un fenomeno aleatorio.
cites work: Q4139463
cites work: Finite exchangeable sequences
cites work: Algorithms for Model-Based Gaussian Hierarchical Clustering
cites work: How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
cites work: Hedonic housing prices and the demand for clean air
cites work: Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting
cites work: The exchangeable multinomial model as an approach to testing deterministic axioms of choice and measurement
cites work: A data envelopment analysis-based clustering approach under dynamic situations
cites work: The Measurement of Observer Agreement for Categorical Data
cites work: A decision analytic approach to predicting quality of life for lung transplant recipients: a hybrid genetic algorithms-based methodology
cites work: Mixture Densities, Maximum Likelihood and the EM Algorithm
cites work: Estimating the dimension of a model
cites work: A Hybrid Clustering Method for Identifying High-Density Clusters
DOI: 10.1016/J.EJOR.2023.09.039
description: scientific article; zbMATH DE number 7881393