galts: Genetic Algorithms and C-Steps Based LTS (Least Trimmed Squares) Estimation

Includes the ga.lts() function that estimates LTS (Least Trimmed Squares) parameters using genetic algorithms and C-steps. ga.lts() constructs a genetic algorithm to form a basic subset and iterates C-steps as defined in Rousseeuw and van-Driessen (2006) to calculate the cost value of the LTS criterion. OLS (Ordinary Least Squares) regression is known to be sensitive to outliers. A single outlying observation can change the values of estimated parameters. LTS is a resistant estimator even the number of outliers is up to half of the data. This package is for estimating the LTS parameters with lower bias and variance in a reasonable time. Version >=1.3 includes the function medmad for fast outlier detection in linear regression.

Package details

AuthorMehmet Hakan Satman
MaintainerMehmet Hakan Satman <>
Package repositoryView on CRAN
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galts documentation built on May 1, 2019, 8:02 p.m.