Title: Multiple deletion diagnostics in beta regression models
Authors: Chien, Li-Chu
Institute of Statistics
Keywords: Beta regression;Multiple outliers;Generalized SWR;Generalized LD;Generalized DFFITS;Generalized DFBETAS
Issue Date: 1-Aug-2013
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.
URI: http://dx.doi.org/10.1007/s00180-012-0370-9
ISSN: 0943-4062
DOI: 10.1007/s00180-012-0370-9
Volume: 28
Issue: 4
Begin Page: 1639
End Page: 1661
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