We discuss a unified framework for the statistical analysis of streaming data obtained by networks with a known neighborhood structure. In particular, we deal with autoregressive models that make explicit the dependence of current observations to their past values and the values of their respective neighborhoods. We consider the case of both continuous and count responses measured over time for each node of a known network. We discuss least squares and quasi maximum likelihood inference. Both methods provide estimators with good properties. In particular, we show that consistent and asymptotically normal estimators of the model parameters, under this high-dimensional data generating process, are obtained after optimizing a criterion function. The methodology is illustrated by applying it to wind speed observed over different weather stations of England and Wales.

Armillotta, M., Fokianos, K., Krikidis, I. (2022). Generalized Linear Models Network Autoregression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.112-125). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-97240-0_9].

Generalized Linear Models Network Autoregression

Armillotta, Mirko;
2022-01-01

Abstract

We discuss a unified framework for the statistical analysis of streaming data obtained by networks with a known neighborhood structure. In particular, we deal with autoregressive models that make explicit the dependence of current observations to their past values and the values of their respective neighborhoods. We consider the case of both continuous and count responses measured over time for each node of a known network. We discuss least squares and quasi maximum likelihood inference. Both methods provide estimators with good properties. In particular, we show that consistent and asymptotically normal estimators of the model parameters, under this high-dimensional data generating process, are obtained after optimizing a criterion function. The methodology is illustrated by applying it to wind speed observed over different weather stations of England and Wales.
7th International Conference and School of Network Science, NetSci-X 2022
prt
2022
Rilevanza internazionale
2022
Settore STAT-01/A - Statistica
Settore STAT-02/A - Statistica economica
Settore ECON-05/A - Econometria
English
Adjacency matrix
Autocorrelation
Least squares estimation
Link function
Multivariate time series
Network analysis
Quasi-likelihood estimation
Intervento a convegno
Armillotta, M., Fokianos, K., Krikidis, I. (2022). Generalized Linear Models Network Autoregression. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp.112-125). GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-97240-0_9].
Armillotta, M; Fokianos, K; Krikidis, I
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/396628
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