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Robust regression diagnostics with data transformations

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  • Cheng, Tsung-Chi

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  • Cheng, Tsung-Chi, 2005. "Robust regression diagnostics with data transformations," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 875-891, June.
  • Handle: RePEc:eee:csdana:v:49:y:2005:i:3:p:875-891
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    References listed on IDEAS

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    1. Hadi, Ali S. & Luceno, Alberto, 1997. "Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 25(3), pages 251-272, August.
    2. Alan D. Chave & David J. Thomson, 2003. "A bounded influence regression estimator based on the statistics of the hat matrix," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(3), pages 307-322, July.
    3. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, vol. 71(1), pages 1-8, April.
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    Cited by:

    1. 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.
    2. Cheng, Tsung-Chi & Biswas, Atanu, 2008. "Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2042-2065, January.
    3. Cheng, Tsung-Chi, 2011. "Robust diagnostics for the heteroscedastic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1845-1866, April.
    4. Conceição Amado & Ana M. Bianco & Graciela Boente & Isabel M. Rodrigues, 2025. "Robust estimation of heteroscedastic regression models: a brief overview and new proposals," Statistical Papers, Springer, vol. 66(3), pages 1-30, April.
    5. Marazzi, Alfio & Yohai, Victor J., 2006. "Robust Box-Cox transformations based on minimum residual autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2752-2768, June.

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