K. (1993) Parallel algorithms for least median of squares regression. Computational Statistics and Data Analysis , 16 , 349–62.Xu, C.W., Shiue, W.K., 1993. Parallel algorithms for least median of squares regre
我们在之前几篇文章中看了鲁棒回归(Robust Regression)中的几个方法: M估计(M-Estimator)的迭代重加权最小二乘法(IRLS)求解,它通过鲁棒损失函数来剔除野点的影响 基于Max-Mixture混合模型的估计,处理残差异方差的情况 鲁棒回归之Least Trimmed Squares 鲁棒回归之RANSAC 鲁棒回归之Least Median of Squares(LMedS) 简...
eigenvalue of the matrix XT WX for the confidence measure is indeed similar to using the condition number in normal least-squares regression, the main difference being that the diagonal weighting matrix W is computed from the LMedS procedure and reflects the inlier/outlier partition of the data....
“Least Median of Squares Regression.”Journal of the American Statistical Association 79 (December 1984), 871–880. Rousseeuw, Peter J., and Leroy, Annick M.Robust Regression and Outlier Detection. New York, NY: John Wiley and Sons, 1987. Rousseeuw, Peter J., and van Zomeran, Burt. “...
Theory Methods 24 1971-1984. MR1345230Stromberg, A.J., 1995, Consistency of the least median of squares estimator in nonlinear regression, Communications in Statistics - Theory and Methods 24, 1971-1984.Stromberg, A.J., Consistency of the least median of squares estimator in nonlinear regression...
The outlier detection in multiple linear regression is a difficult problem because of the masking effect. A procedure that works successfully uses residuals based on a high breakdown estimator. The least trimmed squares (LTS) estimator, which was proposed by Rousseeuw (J. Amer. Statist. Assoc. 79...
Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more robust versions of this estimator by replacing the square by something else, such as the absolute value. In this article a different approach is introduced in which the sum ...
Least median of squares and iteratively re‐weighted least squares as robust linear regression methods for fluorimetric determination of α‐lipoic acid in capsules in ideal and non‐ideal cases of linearityfluorescence quenchingiteratively re‐weighted least squares...
Least Median of Squares, that outperforms some existing solutions in its accuracy or speed. We demonstrate how to minimise new non-differentiable performance function by a deterministic approximate method. Results of simulations and comparison with other learning methods are demonstrated. Improved ...
In multiple regression, the standard HBE's have been those defined by the least median of squares (LMS) and the least trimmed squares (LTS) criteria. Both criteria lead to a partitioning of the data set's n cases into two “halves” – the covered “half” of cases are accommodated by...