Computing Least Median of Squares Regression Lines and Guided Topological Sweep

Abstract
Given a set of data points pi = (xi, yi ) for 1 ≤ in, the least median of squares regression line is a line y = ax + b for which the median of the squared residuals is a minimum over all choices of a and b. An algorithm is described that computes such a line in O(n 2) time and O(n) memory space, thus improving previous upper bounds on the problem. This algorithm is an application of a general method built on top of the topological sweep of line arrangements.

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