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A data-driven reversible jump for estimating a finite mixture of regression models

Author

Listed:
  • Gustavo Alexis Sabillón

    (Universidade Federal de São Carlos)

  • Luiz Gabriel Fernandes Cotrim

    (Boa Vista SCPC)

  • Daiane Aparecida Zuanetti

    (Universidade Federal de São Carlos)

Abstract

We propose a data-driven reversible jump (DDRJ) method for selecting and estimating a mixture of regression models in a single run, which can also be applied as a robust regression model to outliers. We compare the clustering and estimation performance of the proposed method with Expectation–Maximization and Gibbs sampler algorithms combined with model selection criteria in synthetic data sets. Under tested conditions, DDRJ outperforms these traditional methods in identifying the number of groups, classification and precision of estimates. When compared with traditional reversible jump algorithms, the data-driven procedure simplifies the calculations and implementation and shows a better mixing and faster convergence. Finally, we apply the proposed method to analyze two well-studied data sets: tone perception (a simple data set) and baseball salaries (a more complex data set with a larger number of covariates).

Suggested Citation

  • Gustavo Alexis Sabillón & Luiz Gabriel Fernandes Cotrim & Daiane Aparecida Zuanetti, 2023. "A data-driven reversible jump for estimating a finite mixture of regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 350-369, March.
    • Handle: RePEc:spr:testjl:v:32:y:2023:i:1:d:10.1007_s11749-022-00835-w
    DOI: 10.1007/s11749-022-00835-w
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    References listed on IDEAS

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