  This study is motivated by the problem of evaluating reliable false alarm
(FA) rates for sinusoid detection tests applied to unevenly sampled time series
involving colored noise, when a (small) training data set of this noise is
available. While analytical expressions for the FA rate are out of reach in
this situation, we show that it is possible to combine specific periodogram
standardization and bootstrap techniques to consistently estimate the FA rate.
We also show that the procedure can be improved by using generalized
extremevalue distributions. The paper presents several numerical results
including a case study in exoplanet detection from radial velocity data.
