Nonparametric trend estimation in functional time series with application to annual mortality rates.
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Spatio-Temporal Statistics and Data Analysis Group
Preprint Posting Date2020-01-14
Permanent link to this recordhttp://hdl.handle.net/10754/661054
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AbstractHere, we address the problem of trend estimation for functional time series. Existing contributions either deal with detecting a functional trend or assuming a simple model. They consider neither the estimation of a general functional trend nor the analysis of functional time series with a functional trend component. Similarly to univariate time series, we propose an alternative methodology to analyze functional time series, taking into account a functional trend component. We propose to estimate the functional trend by using a tensor product surface that is easy to implement, to interpret, and allows to control the smoothness properties of the estimator. Through a Monte Carlo study, we simulate different scenarios of functional processes to show that our estimator accurately identifies the functional trend component. We also show that the dependency structure of the estimated stationary time series component is not significantly affected by the error approximation of the functional trend component. We apply our methodology to annual mortality rates in France.
CitationMartínez-Hernández, I., & Genton, M. G. (2020). Nonparametric trend estimation in functional time series with application to annual mortality rates. Biometrics. doi:10.1111/biom.13353
SponsorsThis research was supported by the King Abdullah University of Science and Technology (KAUST).
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