Least trimmed squares (LTS) is a statistical technique for estimation of unknown parameters of a linear regression model and provides a ``robust'' alternative to the classical regression method based on minimizing the sum of squared residuals.
This chapter helps to understand the main ideas of robust statistics that stand behind the least trimmed squares estimator and to find out how to use XploRe for this type of robust estimation. As it is impossible to provide a profound introduction into this area here, we refer readers for further information to the bibliography.
Before proceeding to the next section, please type at the XploRe command line
library("metrics")to load the necessary quantlibs (libraries). Quantlib metrics automatically loads xplore , kernel , glm , and multi quantlibs.