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The Catline for Deep Regression

Mia Hubert and Peter Rousseeuw ()

Journal of Multivariate Analysis, 1998, vol. 66, issue 2, 270-296

Abstract: Motivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecatline, a new method for simple linear regression. At any bivariate data setZn={(xi, yi);i=1, ..., n} its regression depth is at leastn/3. This lower bound is attained for data lying on a convex or concave curve, whereas for perfectly linear data the catline attains a depth ofn. We construct anO(n log n) algorithm for the catline, so it can be computed fast in practice. The catline is Fisher-consistent at any linear modely=[beta]x+[alpha]+ein which the error distribution satisfies med(e  x)=0, which encompasses skewed and/or heteroscedastic errors. The breakdown value of the catline is 1/3, and its influence function is bounded. At the bivariate gaussian distribution its asymptotic relative efficiency compared to theL1line is 79.3% for the slope, and 88.9% for the intercept. The finite-sample relative efficiencies are in close agreement with these values. This combination of properties makes the catline an attractive fitting method.

Keywords: algorithm; breakdown; value; heteroscedasticity; influence; function; regression; depth; robust; regression (search for similar items in EconPapers)
Date: 1998
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