Nonparametric trend estimation in functional time series with application to annual mortality rates.
Biometrics
; 77(3): 866-878, 2021 09.
Article
em En
| MEDLINE
| ID: mdl-32797623
ABSTRACT
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Método de Monte Carlo
Tipo de estudo:
Health_economic_evaluation
País/Região como assunto:
Europa
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Arábia Saudita