  We present a multimoment technique for signal classification and apply it to
the detection of fast radio transients in incoherently dedispersed data.
Specifically, we define a spectral modulation index in terms of the fractional
variation in intensity across a spectrum. A signal whose intensity is
distributed evenly across the entire band has a much lower modulation index
than a spectrum with the same intensity localized in a single channel. We are
interested in broadband pulses and use the modulation index to excise
narrowband radio frequency interference (RFI) by applying a modulation index
threshold above which candidate events are removed. The technique is tested
both with simulations and using data from sources of known radio pulses (RRAT
J1928+15 and giant pulses from the Crab pulsar). We find that our technique is
effective at eliminating not only narrowband RFI but also spurious signals from
bright, real pulses that are dedispersed at incorrect dispersion measures. The
method is generalized to coherent dedispersion, image cubes, and astrophysical
narrowband signals that are steady in time. We suggest that the modulation
index, along with other statistics using higher-order moments, should be
incorporated into signal detection pipelines to characterize and classify
signals.
