Abstract
In linear regression, an important role is played by the least quantile of squares (LQS) estimate, which involves the minimization of the qth smallest squared residual for a given set of data. This function is nondifferentiable and nonconvex and may have a large number of local minima. This paper is mainly concerned with the efficient calculation of the global solution, and some different approaches are considered.
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