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Detecting smooth changes in locally stationary processes

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Inference for Change-Point and Related Processes

In a wide range of time series applications, the stochastic properties of the data change over time. It is often realistic to assume that the properties are approximately the same over short time periods and then gradually start to vary. This behaviour is well modelled by locally stationary processes. In this talk, we investigate the question how to estimate time spans where the stochastic features of a locally stationary time series are the same. We set up a general method which allows to deal with a wide variety of features including the mean, covariances, higher moments and the distribution of the time series under consideration.

This talk is part of the Isaac Newton Institute Seminar Series series.

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