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A class of residuals for outlier identification in zero adjusted regression models

Author

Listed:
  • Gustavo H. A. Pereira
  • Juliana Scudilio
  • Manoel Santos-Neto
  • Denise A. Botter
  • Mônica C. Sandoval

Abstract

Zero adjusted regression models are used to fit variables that are discrete at zero and continuous at some interval of the positive real numbers. Diagnostic analysis in these models is usually performed using the randomized quantile residual, which is useful for checking the overall adequacy of a zero adjusted regression model. However, it may fail to identify some outliers. In this work, we introduce a class of residuals for outlier identification in zero adjusted regression models. Monte Carlo simulation studies and two applications suggest that one of the residuals of the class introduced here has good properties and detects outliers that are not identified by the randomized quantile residual.

Suggested Citation

  • Gustavo H. A. Pereira & Juliana Scudilio & Manoel Santos-Neto & Denise A. Botter & Mônica C. Sandoval, 2020. "A class of residuals for outlier identification in zero adjusted regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(10), pages 1833-1847, July.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:10:p:1833-1847
    DOI: 10.1080/02664763.2019.1696759
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    Cited by:

    1. Tiago M. Magalhães & Gustavo H. A. Pereira & Denise A. Botter & Mônica C. Sandoval, 2024. "Bartlett corrections for zero-adjusted generalized linear models," Statistical Papers, Springer, vol. 65(4), pages 2191-2209, June.

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