A modified EM-type algorithm to estimate semi-parametric mixtures of non-parametric regressions

DOI10.1007/s11222-024-10435-3zbMATH Open1542.62025MaRDI QIDQ6581653

Salomon M. Millard, Frans H. J. Kanfer, Sphiwe B. Skhosana

Publication date: 31 July 2024

Published in: Statistics and Computing (Search for Journal in Brave)

zbMATH Keywords

EM algorithmmixture modelslocal-likelihoodlocal-polynomial regressionGaussian mixtures of regressions

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Cites Work

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• Semiparametric mixtures of nonparametric regressions
• Linear smoothers and additive models
• A maximum likelihood methodology for clusterwise linear regression
• Medical applications of finite mixture models
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Finite mixture and Markov switching models.
• Model-based clustering
• Local Likelihood Estimation
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Statistical analysis of finite mixture distributions
• Design-adaptive Nonparametric Regression
• Estimating Mixtures of Normal Distributions and Switching Regressions
• Finite mixture models
• Handbook of Mixture Analysis
• A New Approach to Estimating Switching Regressions
• The Elements of Statistical Learning

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