Nonparametric trend estimation in functional time series with application to annual mortality rates.
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Martinez‐Hernandez&Genton 2020- Biometrics. .pdf
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Accepted manuscript
Type
ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionSpatio-Temporal Statistics and Data Analysis Group
Statistics Program
Date
2020-08-27Preprint Posting Date
2020-01-14Submitted Date
2019-09-09Permanent link to this record
http://hdl.handle.net/10754/661054
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Here, 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.Citation
Martínez-Hernández, I., & Genton, M. G. (2020). Nonparametric trend estimation in functional time series with application to annual mortality rates. Biometrics. doi:10.1111/biom.13353Sponsors
This research was supported by the King Abdullah University of Science and Technology (KAUST).Publisher
WileyJournal
BiometricsPubMed ID
32797623arXiv
2001.04660Additional Links
https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13353ae974a485f413a2113503eed53cd6c53
10.1111/biom.13353