Multiple deletion diagnostics in beta regression models

Li-Chu Chien ()

Computational Statistics, 2013, vol. 28, issue 4, 1639-1661

Abstract: We consider the problem of identifying multiple outliers in a general class of beta regression models proposed by Ferrari and Cribari-Neto (J Appl Stat 31:799–815, 2004 ). The currently available single-case deletion diagnostic measures, e.g., the standardized weighted residual (SWR), the Cook-like distance (LD), etc., often fail to identify multiple outlying observations, because they suffer from the well-known problems of masking and swamping effects. In this article, we develop group deletion diagnostic measures, such as generalized SWR, generalized LD, generalized DFFITS and generalized DFBETAS, and suggest a simple procedure for identifying multiple outliers using these. The performance of the proposed methods is investigated through simulation studies and two practical examples. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Beta regression; Multiple outliers; Generalized SWR; Generalized LD; Generalized DFFITS; Generalized DFBETAS (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:28:y:2013:i:4:p:1639-1661

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DOI: 10.1007/s00180-012-0370-9

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