Computing least median of squares regression lines and guided topological sweep

H. Edelsbrunner, D. Souvaine, Journal of the American Statistical Association 85 (1990) 115–119.

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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.
Publishing Year
Date Published
1990-01-01
Journal Title
Journal of the American Statistical Association
Volume
85
Issue
409
Page
115 - 119
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Edelsbrunner H, Souvaine D. Computing least median of squares regression lines and guided topological sweep. Journal of the American Statistical Association. 1990;85(409):115-119. doi:10.1080/01621459.1990.10475313
Edelsbrunner, H., & Souvaine, D. (1990). Computing least median of squares regression lines and guided topological sweep. Journal of the American Statistical Association, 85(409), 115–119. https://doi.org/10.1080/01621459.1990.10475313
Edelsbrunner, Herbert, and Diane Souvaine. “Computing Least Median of Squares Regression Lines and Guided Topological Sweep.” Journal of the American Statistical Association 85, no. 409 (1990): 115–19. https://doi.org/10.1080/01621459.1990.10475313.
H. Edelsbrunner and D. Souvaine, “Computing least median of squares regression lines and guided topological sweep,” Journal of the American Statistical Association, vol. 85, no. 409, pp. 115–119, 1990.
Edelsbrunner H, Souvaine D. 1990. Computing least median of squares regression lines and guided topological sweep. Journal of the American Statistical Association. 85(409), 115–119.
Edelsbrunner, Herbert, and Diane Souvaine. “Computing Least Median of Squares Regression Lines and Guided Topological Sweep.” Journal of the American Statistical Association, vol. 85, no. 409, American Statistical Association, 1990, pp. 115–19, doi:10.1080/01621459.1990.10475313.

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