A class of autoregressive models for spatial circular data is proposed by assuming that samples of angular measurements are drawn from a multivariate von Mises distribution with mean and concentration parameters that depend on covariates through link functions. The model can flexibly accommodate heteroscedasticity and specific autoregressive correlation structures. Because the computation of the normalizing constant of the multivariate von Mises distribution is unfeasible, inference is based on a computationally tractable Monte Carlo approximation of the log-likelihood. These methods are illustrated on a case study of marine currents in the Northern Adriatic sea.

Lagona, F. (2022). Spatial Autoregressive Models for Circular Data. In B.C.A. Ashis SenGupta (a cura di), Forum for Interdisciplinary Mathematics (pp. 297-313). Springer [10.1007/978-981-19-1044-9_16].

Spatial Autoregressive Models for Circular Data

Lagona F.
2022

Abstract

A class of autoregressive models for spatial circular data is proposed by assuming that samples of angular measurements are drawn from a multivariate von Mises distribution with mean and concentration parameters that depend on covariates through link functions. The model can flexibly accommodate heteroscedasticity and specific autoregressive correlation structures. Because the computation of the normalizing constant of the multivariate von Mises distribution is unfeasible, inference is based on a computationally tractable Monte Carlo approximation of the log-likelihood. These methods are illustrated on a case study of marine currents in the Northern Adriatic sea.
978-981-19-1043-2
Lagona, F. (2022). Spatial Autoregressive Models for Circular Data. In B.C.A. Ashis SenGupta (a cura di), Forum for Interdisciplinary Mathematics (pp. 297-313). Springer [10.1007/978-981-19-1044-9_16].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/412639
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