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Quantile regression versus regression diagnostics in presence of outliers

Author Affiliations

  • 1Department of Statistics, University of Calcutta, India

Res. J. Mathematical & Statistical Sci., Volume 7, Issue (3), Pages 19-21, September,12 (2019)

Abstract

Regression diagnostics are measures computed from the data in order to detect the influential points, following which the outliers can be corrected or removed and the ordinary least square regression may be fitted to the remaining observations. On the other hand, robust regression techniques try to devise estimators that are not so strongly affected by outliers by eliminating the effects of unusually high residuals due to the presence of outliers. This paper aims at comparing the two methods in a simulated data set containing few outlier values. Quantile regression serves as one of the many robust regression techniques.

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