Estimation of prescribed false alarm rate thresholds from local data using tolerance intervals

We present a method for estimating threshold values for signal detection and classification systems in which a prescribed value of false alarm probability is needed. The threshold values are determined directly from observed test statistic data without knowledge of the probability distribution of the data. Our method uses the concept of tolerance intervals from nonparametric statistics.