Modelling informative time points: an evolutionary process approach

• Type: Article in International Scientific Journal
• Date: 2021

Authors

Monteiro, A (Author) Other; Menezes, R (Author) Other; Maria Eduarda Silva (Author) FEP

Journal

Title: Test

Vol. 30 No. 2

Pages: 364-382

ISSN: 1133-0686

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-00S-CAY

DOI: 10.1007/s11749-020-00722-2

Abstract (EN): Real time series sometimes exhibit various types of "irregularities": missing observations, observations collected not regularly over time for practical reasons, observation times driven by the series itself, or outlying observations. However, the vast majority of methods of time series analysis are designed for regular time series only. A particular case of irregularly spaced time series is that in which the sampling procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modelled and the times of the observations. In this work, we propose a model in which the sampling design depends on all past history of the observed processes. Taking into account the natural temporal order underlying available data represented by a time series, then a modelling approach based on evolutionary processes seems a natural choice. We consider maximum likelihood estimation of the model parameters. Numerical studies with simulated and real data sets are performed to illustrate the benefits of this model-based approach.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19