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Robust influence diagnostics for generalized linear models with continuous responses

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  • Li-Chu Chien
  • Tsung-Shan Tsou

Abstract

type="main"> This article introduces two parametric robust diagnostic methods for detecting influential observations in the setting of generalized linear models with continuous responses. The legitimacy of the two proposed methods requires no knowledge of the true underlying distributions so long as their second moments exist. The performance of the two proposed influence diagnostic tools is investigated through limited simulation studies and the analyses of an illustration.

Suggested Citation

  • Li-Chu Chien & Tsung-Shan Tsou, 2014. "Robust influence diagnostics for generalized linear models with continuous responses," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(4), pages 324-343, November.
    • Handle: RePEc:bla:stanee:v:68:y:2014:i:4:p:324-343
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    File URL: http://hdl.handle.net/10.1111/stan.12044
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    References listed on IDEAS

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    1. Richard Royall & Tsung‐Shan Tsou, 2003. "Interpreting statistical evidence by using imperfect models: robust adjusted likelihood functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 391-404, May.
    2. Espinheira, Patri­cia L. & Ferrari, Silvia L.P. & Cribari-Neto, Francisco, 2008. "Influence diagnostics in beta regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4417-4431, May.
    3. 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.
    4. Cheng, Tsung-Chi, 2005. "Robust regression diagnostics with data transformations," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 875-891, June.
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    1. Yonghui Liu & Ruochen Sang & Shuangzhe Liu, 2017. "Diagnostic analysis for a vector autoregressive model under Student-super-′s t-distributions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(2), pages 86-114, May.

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