Abstract
We develop methodology for network data with special attention to epidemic network spatio-temporal structures. We provide estimation methodology for linear network autoregressive models for both continuous and count multivariate time series. A study of non-linear models for inference under the assumption of known network structure is provided. We propose a family of test statistics for testing linearity of the imposed model. In particular, we compare empirically two bootstrap versions of a supremum-type quasi-score test. Synthetic data are employed to demonstrate the validity of the methodological results. Finally, an epidemic application of the proposed methodology to daily COVID-19 cases detected on province-level geographical network in Italy complements the work.
| Original language | English |
|---|---|
| Title of host publication | Data Science in Applications |
| Editors | Gintautas Dzemyda, Jolita Bernatavičienė, Janusz Kacprzyk |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 99-116 |
| Number of pages | 18 |
| ISBN (Electronic) | 9783031244537 |
| ISBN (Print) | 9783031244520, 9783031244551 |
| DOIs | |
| Publication status | Published - 2023 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Publisher | Springer |
| Volume | 1084 |
| ISSN (Print) | 1860-949X |
| ISSN (Electronic) | 1860-9503 |
Bibliographical note
Funding Information:Acknowledgements This work has been co-financed by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation, under the project INFRASTRUCTURES/1216/0017 (IRIDA).
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Keywords
- Bootstrap
- Contraction
- Hypothesis testing
- Identification
- Increasing dimension
- Multivariate time series
- Network analysis
- Nonlinear autoregression
- Nuisance parameter
- Quasi-likelihood
- Score test
