@article{4064,
abstract = {Given a set of data points pi = (xi, yi ) for 1 ≤ i ≤ n, 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.},
author = {Herbert Edelsbrunner and Souvaine, Diane L},
journal = {Journal of the American Statistical Association},
number = {409},
pages = {115 -- 119},
publisher = {American Statistical Association},
title = {{Computing least median of squares regression lines and guided topological sweep}},
doi = {10.1080/01621459.1990.10475313},
volume = {85},
year = {1990},
}