---
res:
bibo_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.@eng
bibo_authorlist:
- foaf_Person:
foaf_givenName: Herbert
foaf_name: Edelsbrunner, Herbert
foaf_surname: Edelsbrunner
foaf_workInfoHomepage: http://www.librecat.org/personId=3FB178DA-F248-11E8-B48F-1D18A9856A87
orcid: 0000-0002-9823-6833
- foaf_Person:
foaf_givenName: Diane
foaf_name: Souvaine, Diane
foaf_surname: Souvaine
bibo_doi: 10.1080/01621459.1990.10475313
bibo_issue: '409'
bibo_volume: 85
dct_date: 1990^xs_gYear
dct_isPartOf:
- http://id.crossref.org/issn/0003-1291
- http://id.crossref.org/issn/1537-274X
dct_language: eng
dct_publisher: American Statistical Association@
dct_title: Computing least median of squares regression lines and guided topological
sweep@
...