Linear Discriminant Functions for Stationary Time Series with Maximize Discriminant Distances

Linear Discriminant Functions for Stationary Time Series with Maximize Discriminant Distances

. Linear functions for classifying an observation as coming from one of two stationary time series which maximize the Chernoff distance, the Hellinger distance or the Kullback-Leibler distance are studied here. We obtain optimal linear discriminant function and show that the resulting linear procedure belongs to the Anderson-Bahadur admissible class. We compare linear discriminant functions for (1) and AR(1) by using simulated data. The asymptotic form of the discriminant function is also studied.