A Practical Approximation Algorithm for the LMS Line Estimator

The problem of fitting a straight line to a finite collection of points in the plane is an important problem in statistical estimation. Robust estimators are widely used because of their lack of sensitivity to outlying data points. The least median-of-squares (LMS) regression line estimator is among the best known robust estimators. Given a set of n points in the plane, it is defined to be the line that minimizes the median squared residual or, more generally, the line that minimizes the residual of any given quantile q, where

• Type: Conference
• Year: 1997
• Where: SODA
• Authors: David M. Mount, Nathan S. Netanyahu, Kathleen Romanik, Ruth Silverman, Angela Y. Wu
• Keywords: Algorithms, Regression Line Estimator, Robust Estimators, SODA 1997, Straight Line