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New Statistical Residuals for Regression Models in the Exponential Family: Characterization, Simulation, Computation, and Applications

Author

Listed:
  • Raydonal Ospina

    (Departamento de Estatística, CASTLab, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
    Departamento de Estatística, LInCa, Universidade Federal da Bahia, Salvador 40170-110, Brazil)

  • Patrícia L. Espinheira

    (Departamento de Estatística, CASTLab, Universidade Federal de Pernambuco, Recife 50670-901, Brazil
    Departamento de Estatística, LInCa, Universidade Federal da Bahia, Salvador 40170-110, Brazil)

  • Leilo A. Arias

    (Departamento de Estatística, CASTLab, Universidade Federal de Pernambuco, Recife 50670-901, Brazil)

  • Cleber M. Xavier

    (Departamento de Estatística e Ciências Atuariais, Universidade Federal de Sergipe, São Cristóvão 49107-230, Brazil)

  • Víctor Leiva

    (School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile)

  • Cecilia Castro

    (Centre of Mathematics, Universidade do Minho, 4710-057 Braga, Portugal)

Abstract

Residuals are essential in regression analysis for evaluating model adequacy, validating assumptions, and detecting outliers or influential data. While traditional residuals perform well in linear regression, they face limitations in exponential family models, such as those based on the binomial and Poisson distributions, due to heteroscedasticity and dependence among observations. This article introduces a novel standardized combined residual for linear and nonlinear regression models within the exponential family. By integrating information from both the mean and dispersion sub-models, the new residual provides a unified diagnostic tool that enhances computational efficiency and eliminates the need for projection matrices. Simulation studies and real-world applications demonstrate its advantages in efficiency and interpretability over traditional residuals.

Suggested Citation

  • Raydonal Ospina & Patrícia L. Espinheira & Leilo A. Arias & Cleber M. Xavier & Víctor Leiva & Cecilia Castro, 2024. "New Statistical Residuals for Regression Models in the Exponential Family: Characterization, Simulation, Computation, and Applications," Mathematics, MDPI, vol. 12(20), pages 1-44, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3196-:d:1497543
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    References listed on IDEAS

    as
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