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dc.contributor.author | Skhosana, Sphiwe Bonakele![]() |
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dc.contributor.author | Kanfer, Frans H.J.![]() |
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dc.contributor.author | Millard, Sollie M.![]() |
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dc.date.accessioned | 2023-03-06T06:39:29Z | |
dc.date.available | 2023-03-06T06:39:29Z | |
dc.date.issued | 2022-05-21 | |
dc.description | DATA AVAILABILITY STATEMENT : Publicly available datasets were analyzed in this study. This data can be found here: https://databank.worldbank.org/source/world-development-indicators/ accessed on 15 February 2022. | en_US |
dc.description.abstract | The non-parametric Gaussian mixture of regressions (NPGMRs) model serves as a flexible approach for the determination of latent heterogeneous regression relationships. This model assumes that the component means, variances and mixing proportions are smooth unknown functions of the covariates where the error distribution of each component is assumed to be Gaussian and hence symmetric. These functions are estimated over a set of grid points using the Expectation- Maximization (EM) algorithm to maximise the local-likelihood functions. However, maximizing each local-likelihood function separately does not guarantee that the local responsibilities and corresponding labels, obtained at the E-step of the EM algorithm, align at each grid point leading to a label-switching problem. This results in non-smooth estimated component regression functions. In this paper, we propose an estimation procedure to account for label switching by tracking the roughness of the estimated component regression functions. We use the local responsibilities to obtain a global estimate of the responsibilities which are then used to maximize each local-likelihood function. The performance of the proposed procedure is demonstrated using a simulation study and through an application using real world data. In the case of well-separated mixture regression components, the procedure gives similar results to competitive methods. However, in the case of poorly separated mixture regression components, the procedure outperforms competitive methods. | en_US |
dc.description.department | Statistics | en_US |
dc.description.librarian | am2023 | en_US |
dc.description.sponsorship | STATOMET, the Bureau for Statistical and Survey Methodology at the University of Pretoria. | en_US |
dc.description.uri | https://www.mdpi.com/journal/symmetry | en_US |
dc.identifier.citation | Skhosana, S.B.; Kanfer, F.H.J.; Millard, S.M. Fitting Non-Parametric Mixture of Regressions: Introducing an EM-Type Algorithm to Address the Label-Switching Problem. Symmetry 2022, 14, 1058. https://DOI.org/10.3390/sym14051058. | en_US |
dc.identifier.issn | 2073-8994 (online) | |
dc.identifier.other | 10.3390/sym14051058 | |
dc.identifier.uri | https://repository.up.ac.za/handle/2263/89964 | |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. | en_US |
dc.subject | Mixture models | en_US |
dc.subject | Non-parametric regressions | en_US |
dc.subject | Local-likelihood estimation | en_US |
dc.subject | Label switching | en_US |
dc.subject | Non-parametric Gaussian mixture of regression (NPGMR) | en_US |
dc.subject | Expectation-maximization algorithm | en_US |
dc.title | Fitting non-parametric mixture of regressions : introducing an EM-type algorithm to address the label-switching problem | en_US |
dc.type | Article | en_US |