Model Selection for Gaussian Mixture Models
From MaRDI portal
Publication:2960508
DOI10.5705/ss.2014.105zbMath1468.62307arXiv1301.3558OpenAlexW2962894282MaRDI QIDQ2960508
Kun Zhang, Heng Peng, Tao Huang
Publication date: 17 February 2017
Published in: Statistica Sinica (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1301.3558
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Statistical ranking and selection procedures (62F07)
Related Items (18)
Model selection for the localized mixture of experts models ⋮ Semiparametric Fractional Imputation Using Gaussian Mixture Models for Handling Multivariate Missing Data ⋮ Estimating finite mixtures of ordinal graphical models ⋮ Explaining mixture models through semantic pattern mining and banded matrix visualization ⋮ Penalized proportion estimation for non parametric mixture of regressions ⋮ A new model selection procedure for finite mixture regression models ⋮ Unsupervised learning of mixture regression models for longitudinal data ⋮ Penalized estimation in finite mixture of ultra-high dimensional regression models ⋮ Finite mixture of varying coefficient model: estimation and component selection ⋮ Extending the Gneiting class for modeling spatially isotropic and temporally symmetric vector random fields ⋮ Hybrid Hard-Soft Screening for High-dimensional Latent Class Analysis ⋮ Cluster non‐Gaussian functional data ⋮ Discussion on “Distributional independent component analysis for diverse neuroimaging modalities” by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo ⋮ Component selection for exponential power mixture models ⋮ Robust mixture regression modeling based on two-piece scale mixtures of normal distributions ⋮ A latent class Cox model for heterogeneous time-to-event data ⋮ Estimation and order selection for multivariate exponential power mixture models ⋮ Statistical inference for normal mixtures with unknown number of components
This page was built for publication: Model Selection for Gaussian Mixture Models