@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       = {Edelsbrunner, Herbert and Souvaine, Diane},
  issn         = {1537-274X},
  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},
}