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Detection of outliers in weighted least squares regression

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Abstract

In multiple linear regression model, we have presupposed assumptions (independence, normality, variance homogeneity and so on) on error term. When case weights are given because of variance heterogeneity, we can estimate efficiently regression parameter using weighted least squares estimator. Unfortunately, this estimator is sensitive to outliers like ordinary least squares estimator. Thus, in this paper, we proposed some statistics for detection of outliers in weighted least squares regression.

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Authors and Affiliations

  1. Department of Computer Engineering, Daejin University, 487-800, Kyunggi-Do, Korea

    Bang Yong Sohn & Guk Boh Kim

Authors
  1. Bang Yong Sohn
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  2. Guk Boh Kim
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Correspondence to Bang Yong Sohn.

Additional information

This work was supported by the Dae-Jin University Research Grants in 1996.

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Sohn, B.Y., Kim, G.B. Detection of outliers in weighted least squares regression. Korean J. Comp. & Appl. Math. 4, 441–452 (1997). https://doi.org/10.1007/BF03014491

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  • DOI: https://doi.org/10.1007/BF03014491

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