2. Least Trimmed Squares
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.