Computing least median of squares regression lines and guided topological sweep
Herbert Edelsbrunner
Souvaine, Diane L
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.
American Statistical Association
1990
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doc-type:article
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http://purl.org/coar/resource_type/c_6501
https://research-explorer.app.ist.ac.at/record/4064
Edelsbrunner H, Souvaine D. Computing least median of squares regression lines and guided topological sweep. <i>Journal of the American Statistical Association</i>. 1990;85(409):115-119. doi:<a href="https://doi.org/10.1080/01621459.1990.10475313">10.1080/01621459.1990.10475313</a>
info:eu-repo/semantics/altIdentifier/doi/10.1080/01621459.1990.10475313
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