Semi-parametric segmentation of multiple series using a DP-Lasso strategy

ABSTRACT

We consider a semi-parametric approach to perform the joint segmentation of multiple series sharing a common functional part. We propose an iterative procedure based on Dynamic Programming for the segmentation part and Lasso estimators for the functional part. Our Lasso procedure, based on the dictionary approach, allows us to both estimate smooth functions and functions with local irregularity, which permits more flexibility than previous proposed methods. This yields to a better estimation of the functional part and improvements in the segmentation. The performance of our method is assessed using simulated data and real data from agriculture and geodetic studies. Our estimation procedure results to be a reliable tool to detect changes and to obtain an interpretable estimation of the functional part of the model in terms of known functions.

KEYWORDS:

• Semi-parametric estimation
• multiple series
• segmentation
• Lasso
• dynamic programming
• geodetic data

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by FONDECYT [grant numbers 1141256 and 1141258]; ANILLO [grant number ACT-1112]; mathamsud [grant number 16-MATH-03] SIDRE and CONICYT [grant number 870100003].