A Graphical Technique for Determining the Number of Components in a Mixture of Normals
From MaRDI portal
Publication:4305719
DOI10.2307/2290850zbMath0798.62004OpenAlexW4239027680MaRDI QIDQ4305719
Publication date: 15 September 1994
Full work available at URL: https://doi.org/10.2307/2290850
clustersmoothingmodalitykernel density estimatorlarge deviationsstationary Gaussian processfinite mixture modelbimodalitymodediagnostic plotmixture of normalscomponent estimationdiagnosticfinite number of subpopulationstest for mixing
Density estimation (62G07) Nonparametric hypothesis testing (62G10) Graphical methods in statistics (62A09)
Related Items (40)
Minimum quadratic distance density estimation using nonparametric mixtures ⋮ EM-test for homogeneity in a two-sample problem with a mixture structure ⋮ Testing the order of a normal mixture in mean ⋮ Hypothesis test for normal mixture models: the EM approach ⋮ Modal simulation and visualization in finite mixture models ⋮ Probabilistic models in cluster analysis ⋮ Moment-based oscillation properties of mixture models ⋮ Generalized likelihood‐ratio test of the number of components in finite mixture models ⋮ Semiparametric Mixtures of Generalized Exponential Families ⋮ Unnamed Item ⋮ Robust estimation for the order of finite mixture models ⋮ Generalized weighted likelihood density estimators with application to finite mixture of exponential family distributions ⋮ A practical sampling approach for a Bayesian mixture model with unknown number of compo\-nents ⋮ Integrated cumulative error (ICE) distance for non-nested mixture model selection: application to extreme values in metal fatigue problems ⋮ Test for homogeneity in Hardy-Weinberg normal mixture model ⋮ Weighted tests of homogeneity for testing the number of components in a mixture ⋮ Tail properties and approximate distribution and expansion for extreme of LGMD ⋮ Tests for normal mixtures based on the empirical characteristic function ⋮ A statistical test for mixture detection with application to component identification in multidimensional biomolecular NMR studies ⋮ Covariate selection in mixture models with the censored response variable ⋮ \(L_{2}E\) estimation of mixture complexity for count data ⋮ Bump hunting with non-Gaussian kernels ⋮ Assessing extrema of empirical principal component functions ⋮ Robust estimation in the normal mixture model ⋮ Determining the number of components in mixtures of linear models. ⋮ MCMC for normalized random measure mixture models ⋮ Computation of an efficient and robust estimator in a semiparametric mixture model ⋮ Minimum Hellinger distance estimation for a semiparametric location-shifted mixture model ⋮ Robust estimation of mixing measures in finite mixture models ⋮ Multivariate mode hunting: Data analytic tools with measures of significance ⋮ Gamma Mixture: Bimodality, Inflexions and L-Moments ⋮ Consistency of minimizing a penalized density power divergence estimator for mixing distribution ⋮ Robust estimation of mixture complexity for count data ⋮ Minimum disparity computation via the iteratively reweighted least integrated squares algorithms ⋮ The Mixturegram: A Visualization Tool for Assessing the Number of Components in Finite Mixture Models ⋮ On consistency of the MLE under finite mixtures of location-scale distributions with a structural parameter ⋮ Testing for monotonicity of a regression mean by calibrating for linear functions. ⋮ Multiscale maximum likelihood analysis of a semiparametric model, with applications. ⋮ Empirical identifiability in finite mixture models ⋮ Parameterizing mixture models with generalized moments
This page was built for publication: A Graphical Technique for Determining the Number of Components in a Mixture of Normals