Statistical Modelling 19 (2) (2019), 194–220

A Bayesian approach for the segmentation of series with a functional effect

Meili Baragatti
MISTEA, Montpellier SupAgro,
INRA, CNRS,
Univ Montpellier,
Montpellier,
France.


Karine Bertin
CIMFAV-Facultad de Ingeniería,
Universidad de Valparaíso,
Valparaíso,
Chile.
e-mail: karine.bertin@uv.cl

Emilie Lebarbier
AgroParisTech/INRA UMR518,
Paris 5e,
France.


Cristian Meza
CIMFAV-Facultad de Ingeniería,
Universidad de Valparaíso,
Valparaíso,
Chile.


Abstract:

In some application fields, series are affected by two different types of effects: abrupt changes (or change-points) and functional effects. We propose here a Bayesian approach that allows us to estimate these two parts. Here, the underlying piecewise-constant part (associated to the abrupt changes) is expressed as the product of a lower triangular matrix by a sparse vector and the functional part as a linear combination of functions from a large dictionary where we want to select the relevant ones. This problem can thus lead to a global sparse estimation and a stochastic search variable selection approach is used to this end. The performance of our proposed method is assessed using simulation experiments. Applications to three real datasets from geodesy, agronomy and economy fields are also presented.

Keywords:

Segmentation; functional effect; dictionary approach; Bayesian inference; variable selection.

Downloads:

Example data and code in zipped archive.
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