The expression “robust regression” denotes a set of estimation techniques that areless sensitive than ordinary least squares (OLS) to the effect of possible influentialobservations.The main argument invoked to justify the use of robust regression is thatit provides efficiency gains in the presence of errors with heavy-tailed distributionIn its various forms, robust regression has a well established tradition in statistics (see,e.g., Huber, 1981; Hampel, Ronchetti, Rousseeuw and Stahel, 1986; Rousseeuw andLeroy, 1987, and Maronna, Martin and Yohai, 2006)2.1. Set-up and notationWe consider the problem of estimating a regression model of the formyi = x0iβ + εi, i = 1, ..., n,where yi is a scalar, xi and β are k dimensional vectors with kdisturbance.where yi is a scalar, xi and β are k dimensional vectors with kdisturbance.
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