  It is difficult to choose detection thresholds for tests of non-stationarity
that assume {\em a priori} a noise model if the data is statistically
uncharacterized to begin with. This is a potentially serious problem when an
automated analysis is required, as would be the case for the huge data sets
that large interferometric gravitational wave detectors will produce. A
solution is proposed in the form of a {\em robust} time-frequency test for
detecting non-stationarity whose threshold for a specified false alarm rate is
almost independent of the statistical nature of the ambient stationary noise.
The efficiency of this test in detecting bursts is compared with that of an
ideal test that requires prior information about both the statistical
distribution of the noise and also the frequency band of the burst. When
supplemented with an approximate knowledge of the burst duration, this test can
detect, at the same false alarm rate and detection probability, bursts that are
about 3 times larger in amplitude than those that the ideal test can detect.
Apart from being robust, this test has properties which make it suitable as an
online monitor of stationarity.
