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University of Cambridge > Talks.cam > Statistics > Nonparametric regression for locally stationary time series
![]() Nonparametric regression for locally stationary time seriesAdd to your list(s) Download to your calendar using vCal
If you have a question about this talk, please contact Richard Samworth. We study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the main conditions of the theory are satisfied for a large class of nonlinear autoregressive processes with a time-varying regression function. Finally, we examine structured models where the regression function splits up into time-varying additive components. As will be seen, estimation in these models does not suffer from the curse of dimensionality. This talk is part of the Statistics series. This talk is included in these lists:
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