[{"year":"1990","citation":{"short":"H. Edelsbrunner, D. Souvaine, Journal of the American Statistical Association 85 (1990) 115–119.","mla":"Edelsbrunner, Herbert, and Diane Souvaine. “Computing Least Median of Squares Regression Lines and Guided Topological Sweep.” <i>Journal of the American Statistical Association</i>, vol. 85, no. 409, American Statistical Association, 1990, pp. 115–19, doi:<a href=\"https://doi.org/10.1080/01621459.1990.10475313\">10.1080/01621459.1990.10475313</a>.","ista":"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.","apa":"Edelsbrunner, H., &#38; Souvaine, D. (1990). Computing least median of squares regression lines and guided topological sweep. <i>Journal of the American Statistical Association</i>. American Statistical Association. <a href=\"https://doi.org/10.1080/01621459.1990.10475313\">https://doi.org/10.1080/01621459.1990.10475313</a>","ama":"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>","chicago":"Edelsbrunner, Herbert, and Diane Souvaine. “Computing Least Median of Squares Regression Lines and Guided Topological Sweep.” <i>Journal of the American Statistical Association</i>. American Statistical Association, 1990. <a href=\"https://doi.org/10.1080/01621459.1990.10475313\">https://doi.org/10.1080/01621459.1990.10475313</a>.","ieee":"H. Edelsbrunner and D. Souvaine, “Computing least median of squares regression lines and guided topological sweep,” <i>Journal of the American Statistical Association</i>, vol. 85, no. 409. American Statistical Association, pp. 115–119, 1990."},"date_updated":"2022-02-22T15:10:54Z","day":"01","doi":"10.1080/01621459.1990.10475313","abstract":[{"text":"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.","lang":"eng"}],"volume":85,"extern":"1","scopus_import":"1","_id":"4064","issue":"409","author":[{"id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","full_name":"Edelsbrunner, Herbert","orcid":"0000-0002-9823-6833","last_name":"Edelsbrunner","first_name":"Herbert"},{"full_name":"Souvaine, Diane","last_name":"Souvaine","first_name":"Diane"}],"article_processing_charge":"No","date_created":"2018-12-11T12:06:43Z","publication_status":"published","intvolume":"        85","title":"Computing least median of squares regression lines and guided topological sweep","quality_controlled":"1","page":"115 - 119","publisher":"American Statistical Association","article_type":"original","type":"journal_article","date_published":"1990-01-01T00:00:00Z","publication_identifier":{"eissn":["1537-274X"],"issn":["0003-1291"]},"publist_id":"2059","main_file_link":[{"url":"https://www.tandfonline.com/doi/abs/10.1080/01621459.1990.10475313"}],"status":"public","user_id":"ea97e931-d5af-11eb-85d4-e6957dddbf17","publication":"Journal of the American Statistical Association","oa_version":"None","month":"01","language":[{"iso":"eng"}]}]