Outlier detection in multivariate functional data through a contaminated mixture model
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Publication:2157511
DOI10.1016/j.csda.2022.107496OpenAlexW3170467541MaRDI QIDQ2157511
Martial Amovin-Assagba, Julien Jacques, Irène Gannaz
Publication date: 22 July 2022
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2106.07222
EM algorithmmodel-based clusteringoutlier detectionmultivariate functional datacontaminated Gaussian mixture
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- A new family of multivariate heavy-tailed distributions with variable marginal amounts of tailweight: application to robust clustering
- Model-based clustering of time series in group-specific functional subspaces
- Directional outlyingness for multivariate functional data
- The discriminative functional mixture model for a comparative analysis of bike sharing systems
- A proposal for robust curve clustering
- Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA
- Multivariate functional outlier detection
- Regression models for functional data by reproducing kernel Hilbert spaces methods
- Curves discrimination: a nonparametric functional approach
- Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions
- Robust estimation and classification for functional data via projection-based depth notions
- A mixture of mixture models for a classification problem: the unity measure error
- Estimating the dimension of a model
- Trimmed \(k\)-means: An attempt to robustify quantizers
- Model-based clustering for multivariate functional data
- Asymmetric clusters and outliers: mixtures of multivariate contaminated shifted asymmetric Laplace distributions
- Trimmed means for functional data
- Minimal penalties for Gaussian model selection
- Defining probability density for a distribution of random functions
- A survey of outlier detection methodologies
- Multivariate and functional classification using depth and distance
- Functional data clustering: a survey
- Functional data analysis.
- Parsimonious mixtures of multivariate contaminated normal distributions
- Maximum likelihood estimation via the ECM algorithm: A general framework
- Dynamic programming algorithm optimization for spoken word recognition
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Unsupervised Curve Clustering using B‐Splines
- Clustering for Sparsely Sampled Functional Data
- Multiple scaled contaminated normal distribution and its application in clustering
- Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions
- A new look at the inverse Gaussian distribution with applications to insurance and economic data
- On the Identifiability of Finite Mixtures
- A Quantitative Study of Gene Regulation Involved in the Immune Response of Anopheline Mosquitoes
- Dichotomous unimodal compound models: application to the distribution of insurance losses
- A new look at the statistical model identification
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