Least Trimmed Squares Robust Regression

DESCRIPTION:

Carries out least trimmed squares (LTS) regression, as introduced in Rousseeuw (1984). This regression method minimizes the sum of the quan smallest squared residuals. Here, quan is an integer between (roughly) n/2 and n.

This function is generic (see Methods); method functions can be written to handle specific classes of data. Classes which already have methods for this function include:

formula

USAGE:

ltsreg(...) 

REQUIRED ARGUMENTS:

...
methods have additional arguments.

VALUE:

an object of class "lts" representing a robust fit of a linear model. See lts.object for details.

REFERENCES:

Burns, P. J. (1992). A Genetic Algorithm for Robust Regression Estimation. (StatSci Technical Note).

Rousseeuw, P. J. (1984). Least median of squares regression. Journal of the American Statistical Association, 79, 871-881.

Rousseeuw, P. J. and Leroy, A. M. (1987). Robust Regression and Outlier Detection. New York: Wiley.

SEE ALSO:

, , , , , .

EXAMPLES:

ltsreg(stack.x, stack.loss) 
ltsreg(ozone~wind+radiation+temperature, data=air)