Clustering with the multivariate normal inverse Gaussian distribution
DOI10.1016/j.csda.2014.09.006zbMath1468.62151OpenAlexW2029217165WikidataQ63214073 ScholiaQ63214073MaRDI QIDQ1660185
Dimitris Karlis, Paul D. McNicholas, Isobel Claire Gormley, Adrian O'Hagan, Thomas Brendan Murphy
Publication date: 15 August 2018
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10197/6106
model-based clusteringinformation metricsMCLUSTKolmogorov-Smirnov goodness of fitmultivariate normal inverse Gaussian distribution
Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)
Related Items (23)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Mixtures of common factor analyzers for high-dimensional data with missing information
- Multivariate measurement error models using finite mixtures of skew-Student \(t\) distributions
- Methods for merging Gaussian mixture components
- Model-based clustering, classification, and discriminant analysis via mixtures of multivariate \(t\)-distributions
- Inference for mixtures of symmetric distributions
- Analysis of multivariate skew normal models with incomplete data
- Estimating the dimension of a model
- Parsimonious skew mixture models for model-based clustering and classification
- MCLUST: Software for model-based cluster analysis
- Comparing clusterings -- an information based distance
- Measurement of the distance between distinct partitions of a finite set of objects
- Robust statistical modelling using the multivariate skew t distribution with complete and incomplete data
- Bayesian inference for finite mixtures of univariate and multivariate skew-normal and skew-t distributions
- A Look at Some Data on the Old Faithful Geyser
- Model‐based clustering of longitudinal data
- Detecting Features in Spatial Point Processes with Clutter via Model-Based Clustering
- Skewness in Commingled Distributions
- How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis
- Model-Based Gaussian and Non-Gaussian Clustering
- Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- Bayes Factors
- A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
- Calculation of the Modified Bessel Functions of the Second Kind with Complex Argument
- The Kolmogorov-Smirnov Test for Goodness of Fit
This page was built for publication: Clustering with the multivariate normal inverse Gaussian distribution