Rousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79(388):871–880 MathSciNet MATHRousseeuw, P.J. (1984) Least Median of Squares Regression, Journal of the American Statistical Association, 79, 871–8880.Rousseeuw, P.: Least median of squares regression. J. Am. ...
Rousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79: 871–880 Article MathSciNet MATH Google Scholar Rousseeuw AM, Leroy PJ (1987) Robust regression and outlier detection. Wiley, New York Book MATH Google Scholar Rusiecki AL (2005) Fault tolerant feedforward neural...
These methods included least-squares regression, the least absolute -deviation method, the least median of squares method, and two techniques based on an adaptive Kalman filter. For data sets consisting of 4–9 points with one outlier, the average errors in the estimation of the slope were ...
Consistency of the least median of squares estimator in nonlinear regression - Stromberg - 1995Stromberg, A. J., 1995. Consistency of the least median of squares estimator in nonlinear regression. Commun. Statist.: Th. & Meth., 24, 1971-1984....
Least median of squares regression presents a daunting computational problem for moderate to large data sets — the optimum is the Chebyshev regression fit to the correct subset of the cases, and finding it exactly requires an investigation of all subsets of the cases of a certain size. The fea...
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 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. “...
Throwing out outliers with the aim of improving the suitability of the regression equation cannot be done carelessly because it will provide imprecise estimation precision. This study examines the performance of robust method of the least median squares (LMS) and the multi-stage method (MM) ...
Abstract We propose modified p-subset algorithms for computing the least quartile difference and least trimmed difference estimates in a multiple linear regression model. Computational Statistics, Vol. 1, 411–428. Heidelberg: Physica Verlag.
Least squares is probably the best known method for fitting linear models and by far the most widely used. Surprisingly, the discrete L 1 analogue, least a... P Bloomfield,WL Steiger - Birkh?user 被引量: 645发表: 1984年 Estimation of multiple-regime regressions with least absolutes deviatio...