Gaussian and Non-Gaussian Linear Time Series and Random Fields

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The book delves into autoregressive and moving average linear stationary sequences, emphasizing their role in time series analysis, particularly within the Gaussian framework. It contrasts classical results with non-Gaussian scenarios, highlighting the complexities of prediction and estimation in these contexts. Chapter 1 focuses on reversibility in linear stationary sequences, offering necessary conditions and showcasing key findings by Breidt, Davis, and Cheng regarding filter coefficient identifiability in non-Gaussian random fields.