Parameter estimation for mixtures of skew Laplace normal distributions and application in mixture regression modeling

DOI10.1080/03610926.2016.1252400zbMath1462.62195OpenAlexW2547713311MaRDI QIDQ4595875

Fatma Zehra Dogru, Olcay Arslan

Publication date: 6 December 2017

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2016.1252400

zbMATH Keywords

EM algorithmmixture modelMLmixture regression modelSLNSTN

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Robustness and adaptive procedures (parametric inference) (62F35) Probabilistic models, generic numerical methods in probability and statistics (65C20)

Related Items (9)

Robust mixture regression modeling based on the generalized M (GM)-estimation method ⋮ Multivariate cluster weighted models using skewed distributions ⋮ Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions ⋮ Mixture regression modelling based on the shape mixtures of skew Laplace normal distribution ⋮ Parameter estimates for two-way repeated measurement MANOVA based on multivariate Laplace distribution ⋮ Finite mixtures of skew Laplace normal distributions with random skewness ⋮ Pursuing Sources of Heterogeneity in Modeling Clustered Population ⋮ Robust finite mixture regression for heterogeneous targets ⋮ Numerical characteristics and parameter estimation of finite mixed generalized normal distribution

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