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