  A main goal of regression is to derive statistical conclusions on the
conditional distribution of the output variable Y given the input values x. Two
of the most important characteristics of a single distribution are location and
scale. Support vector machines (SVMs) are well established to estimate location
functions like the conditional median or the conditional mean. We investigate
the estimation of scale functions by SVMs when the conditional median is
unknown, too. Estimation of scale functions is important e.g. to estimate the
volatility in finance. We consider the median absolute deviation (MAD) and the
interquantile range (IQR) as measures of scale. Our main result shows the
consistency of MAD-type SVMs.
