An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation
Machine Learning
2025-08-04 v1 Machine Learning
Methodology
Abstract
Zhu and Melnykov (2018) develop a model to fit mixture models when the components are derived from the Manly transformation. Their EM algorithm utilizes Nelder-Mead optimization in the M-step to update the skew parameter, . An alternative EM gradient algorithm is proposed, using one step of Newton's method, when initial estimates for the model parameters are good.
Keywords
Cite
@article{arxiv.2410.00848,
title = {An EM Gradient Algorithm for Mixture Models with Components Derived from the Manly Transformation},
author = {Katharine M. Clark and Paul D. McNicholas},
journal= {arXiv preprint arXiv:2410.00848},
year = {2025}
}