A class of residuals for outlier identification in zero adjusted regression models. Issue 10 (26th July 2020)

• Record Type: Journal Article
• Authors: Pereira, Gustavo H. A.; Scudilio, Juliana; Santos-Neto, Manoel; Botter, Denise A.; Sandoval, Mônica C.
• Is Part Of: Journal of applied statistics. Volume 47:Issue 10(2020)
• Page Start: 1833
• Page End: 1847
• Publication Date: 2020-07-26
• Subjects: Diagnostic analysis -- outliers -- randomized quantile residual -- zero adjusted regression models; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2019.1696759
• Languages: English
• ISSNs: 0266-4763

Abstract

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.