An approximation algorithm for least median of squares regression

✍ Scribed by Clark F. Olson

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 486 KB
• Volume: 63
• Category: Article
• ISSN: 0020-0190
• DOI: 10.1016/s0020-0190(97)00132-4

✦ Synopsis

Least median of squares (LMS) regression is a robust method to fit equations to observed data (typically in a linear model). This paper describes an approximation algorithm for LMS regression. The algorithm generates a regression solution with median residual no more than twice the optimal median residual. Random sampling is used to provide a simple 0( n log' n) expected time algorithm in the two-dimensional case that is successful with high probability. This algorithm is also extended to arbitrary dimension d with 0(&l log n) worst-case complexity for fixed d > 2. @ 1997 Elsevier Science B.V.