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Deletion residuals in the detection of heterogeneity of variances in linear regression

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  • A.H.M. Rahmatullah Imon

Abstract

The heterogeneity of error variance often causes a huge interpretive problem in linear regression analysis. Before taking any remedial measures we first need to detect this problem. A large number of diagnostic plots are now available in the literature for detecting heteroscedasticity of error variances. Among them the 'residuals' and 'fits' (R-F) plot is very popular and commonly used. In the R-F plot residuals are plotted against the fitted responses, where both these components are obtained using the ordinary least squares (OLS) method. It is now evident that the OLS fits and residuals suffer a huge setback in the presence of unusual observations and hence the R-F plot may not exhibit the real scenario. The deletion residuals based on a data set free from all unusual cases should estimate the true errors in a better way than the OLS residuals. In this paper we propose 'deletion residuals' and the 'deletion fits' (DR-DF) plot for the detection of the heterogeneity of error variances in a linear regression model to get a more convincing and reliable graphical display. Examples show that this plot locates unusual observations more clearly than the R-F plot. The advantage of using deletion residuals in the detection of heteroscedasticity of error variance is investigated through Monte Carlo simulations under a variety of situations.

Suggested Citation

  • A.H.M. Rahmatullah Imon, 2009. "Deletion residuals in the detection of heterogeneity of variances in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(3), pages 347-358.
    • Handle: RePEc:taf:japsta:v:36:y:2009:i:3:p:347-358
    DOI: 10.1080/02664760802466237
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    References listed on IDEAS

    as
    1. A. H. M. Rahmatullah Imon, 2003. "Residuals from deletion in added variable plots," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(7), pages 827-841.
    2. 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.
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