  In recent years, a growing need has arisen in different fields for the
development of computational systems for automated analysis of large amounts of
data (high-throughput). Dealing with nonstandard noise structure and outliers,
that could have been detected and corrected in manual analysis, must now be
built into the system with the aid of robust methods. We discuss such problems
and present insights and solutions in the context of behavior genetics, where
data consists of a time series of locations of a mouse in a circular arena. In
order to estimate the location, velocity and acceleration of the mouse, and
identify stops, we use a nonstandard mix of robust and resistant methods:
LOWESS and repeated running median. In addition, we argue that protection
against small deviations from experimental protocols can be handled
automatically using statistical methods. In our case, it is of biological
interest to measure a rodent's distance from the arena's wall, but this measure
is corrupted if the arena is not a perfect circle, as required in the protocol.
The problem is addressed by estimating robustly the actual boundary of the
arena and its center using a nonparametric regression quantile of the
behavioral data, with the aid of a fast algorithm developed for that purpose.
