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On Random Matrix Theory for stationary processes

Random Matrix Theory has generated tremendous interest in recent years, partly from powerful results developed for multi-user detection theory but also for growing applications in statistics, signal processing and econometrics. However the current theory has emphasized white noise data. In this paper we present for the first time some results applicable to temporally stationary processes.