A New Nonstationarity Detector

A new test to determine the stationarity length of a locally wide sense stationary Gaussian random process is proposed. Based on the modeling of the process as a time-varying autoregressive process, the time-varying model parameters are tested using a Rao test. The use of a Rao test avoids the necessity of obtaining the maximum likelihood estimator of the model parameters under the alternative hypothesis, which is intractable. Computer simulation results are given to demonstrate its effectiveness and to verify the asymptotic theoretical performance of the test. Applications are to spectral analysis, noise estimation, and time series modeling.