A regression model for correlated 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 suitable link functions. The model accommodates for heteroscedasticity, unstructured correlation, and specific autoregressive correlation structures. Inference is based on a Monte Carlo approximation of the log-likelihood, due to the intractability of the normalizing constant. The model is illustrated on two case studies: a longitudinal study of animal orientation and a study on the spatial distribution of sea current directions.

Lagona, F. (2014). Regression analysis of correlated circular data based on the multivariate von Mises distribution. In D.B.T. Cabras S (a cura di), 47th SIS Scientific Meeting of the Italian Statistica Society. CAGLIARI : CUEC - Cooperativa Universitaria Editrice Cagliaritana.

Regression analysis of correlated circular data based on the multivariate von Mises distribution

LAGONA, Francesco
2014

Abstract

A regression model for correlated 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 suitable link functions. The model accommodates for heteroscedasticity, unstructured correlation, and specific autoregressive correlation structures. Inference is based on a Monte Carlo approximation of the log-likelihood, due to the intractability of the normalizing constant. The model is illustrated on two case studies: a longitudinal study of animal orientation and a study on the spatial distribution of sea current directions.
978-88-8467-874-4
Lagona, F. (2014). Regression analysis of correlated circular data based on the multivariate von Mises distribution. In D.B.T. Cabras S (a cura di), 47th SIS Scientific Meeting of the Italian Statistica Society. CAGLIARI : CUEC - Cooperativa Universitaria Editrice Cagliaritana.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/173659
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