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Multiple deletion diagnostics in beta regression models

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  • Li-Chu Chien

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

Suggested Citation

  • Li-Chu Chien, 2013. "Multiple deletion diagnostics in beta regression models," Computational Statistics, Springer, vol. 28(4), pages 1639-1661, August.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:4:p:1639-1661
    DOI: 10.1007/s00180-012-0370-9
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    References listed on IDEAS

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    1. A. A. M. Nurunnabi & A.H.M. Rahmatullah Imon & M. Nasser, 2010. "Identification of multiple influential observations in logistic regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1605-1624.
    2. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    3. Simas, Alexandre B. & Barreto-Souza, Wagner & Rocha, Andréa V., 2010. "Improved estimators for a general class of beta regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 348-366, February.
    4. A. H. M. Rahmatullah Imon, 2005. "Identifying multiple influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(9), pages 929-946.
    5. Cribari-Neto, Francisco & Zeileis, Achim, 2010. "Beta Regression in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 34(i02).
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    Cited by:

    1. Yingli Pan & Zhan Liu & Guangyu Song, 2021. "Outlier detection under a covariate-adjusted exponential regression model with censored data," Computational Statistics, Springer, vol. 36(2), pages 961-976, June.

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