scientific article; zbMATH DE number 6860847
From MaRDI portal
Publication:4637074
zbMath1442.62138arXiv1309.6895MaRDI QIDQ4637074
Christian Hennig, Pietro Coretto
Publication date: 17 April 2018
Full work available at URL: https://arxiv.org/abs/1309.6895
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nonparametric robustness (62G35) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Related Items
Fitting insurance and economic data with outliers: a flexible approach based on finite mixtures of contaminated gamma distributions, Model-based clustering, Estimation and computations for Gaussian mixtures with uniform noise under separation constraints, A fuzzy approach to robust regression clustering, An adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture‐based clustering, Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score, Weighted likelihood mixture modeling and model-based clustering, Weighted likelihood latent class linear regression, Statistical analysis of a hierarchical clustering algorithm with outliers
Uses Software
Cites Work
- A fast algorithm for robust constrained clustering
- Strong consistency of \(k\)-parameters clustering
- Maximum likelihood estimation of heterogeneous mixtures of Gaussian and uniform distributions
- Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints
- Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods
- A general trimming approach to robust cluster analysis
- Trimmed ML estimation of contaminated mixtures
- Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
- Inference for multivariate normal mixtures
- On the convergence properties of the EM algorithm
- A constrained formulation of maximum-likelihood estimation for normal mixture distributions
- Maximum-likelihood estimation of the parameters of a multivariate normal distribution
- Note on the consistency of the maximum likelihood estimate for nonidentifiable distributions
- Trimmed \(k\)-means: An attempt to robustify quantizers
- A robust method for cluster analysis
- Breakdown points for maximum likelihood estimators of location-scale mixtures
- Weak convergence and empirical processes. With applications to statistics
- A note on the article ``Inference for multivariate normal mixtures by J. Chen and X. Tan
- A simulation study to compare robust clustering methods based on mixtures
- A likelihood-based constrained algorithm for multivariate normal mixture models
- Maximum likelihood estimation via the ECM algorithm: A general framework
- Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
- Mixture Densities, Maximum Likelihood and the EM Algorithm
- An inequality with application to multivariate analysis
- Algorithms for Model-Based Gaussian Hierarchical Clustering
- Model-Based Gaussian and Non-Gaussian Clustering
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Robustness Properties of k Means and Trimmed k Means
- Optimal Shrinkage of Singular Values
- Asymptotic Properties of Non-Linear Least Squares Estimators
- Condition-Number-Regularized Covariance Estimation
- Sequential Minimax Search for a Maximum
- Model-Based Clustering, Classification, and Density Estimation Using mclust in R
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
