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

Autor: Pereira GHA; Department of Statistics, Federal University of São Carlos, São Carlos, Brazil., Scudilio J; Department of Statistics, Federal University of São Carlos, São Carlos, Brazil.; Department of Applied Mathematics and Statistics, University of São Paulo, São Paulo, Brazil., Santos-Neto M; Department of Statistics, Federal University of São Carlos, São Carlos, Brazil.; Department of Statistics, Federal University of Campina Grande, Campina Grande, Brazil., Botter DA; Department of Statistics, University of São Paulo, São Paulo, Brazil., Sandoval MC; Department of Statistics, University of São Paulo, São Paulo, Brazil.
Jazyk: angličtina
Zdroj: Journal of applied statistics [J Appl Stat] 2019 Nov 28; Vol. 47 (10), pp. 1833-1847. Date of Electronic Publication: 2019 Nov 28 (Print Publication: 2020).
DOI: 10.1080/02664763.2019.1696759
Abstrakt: 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.
Competing Interests: No potential conflict of interest was reported by the authors.
(© 2019 Informa UK Limited, trading as Taylor & Francis Group.)
Databáze: MEDLINE
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