The modelling of animal movement is an important ecological andenvironmental issue. It is well-known that animals change their movementpatterns over time, according to observable and unobservable factors. To tracethe dynamics of behaviors, to identify factors in uencing these dynamics andunobserved characteristics driving intra-subjects correlations, we introduce atime-dependent mixed eects projected normal regression model. A set ofanimal-specic parameters following a hidden Markov chain is introduced todeal with unobserved heterogeneity. For the maximum likelihood estimationof the model parameters, we outline an Expectation-Maximization algorithm.A large-scale simulation study provides evidence on model behavior. The dataanalysis approach based on the proposed model is nally illustrated by anapplication to a dataset, which derives from a population of Talitrus saltatorfrom the beach of Castiglione della Pescaia (Italy).

Maruotti, A., PUNZO Antonio, M.G., Lagona, F. (2016). A time-dependent extension of the projected Normal regression model for longitudinal circular data based on a hidden Markov heterogeneity structure. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 30, 1725-1740 [10.1007/s00477-015-1183-5].

A time-dependent extension of the projected Normal regression model for longitudinal circular data based on a hidden Markov heterogeneity structure

LAGONA, Francesco
2016

Abstract

The modelling of animal movement is an important ecological andenvironmental issue. It is well-known that animals change their movementpatterns over time, according to observable and unobservable factors. To tracethe dynamics of behaviors, to identify factors in uencing these dynamics andunobserved characteristics driving intra-subjects correlations, we introduce atime-dependent mixed eects projected normal regression model. A set ofanimal-specic parameters following a hidden Markov chain is introduced todeal with unobserved heterogeneity. For the maximum likelihood estimationof the model parameters, we outline an Expectation-Maximization algorithm.A large-scale simulation study provides evidence on model behavior. The dataanalysis approach based on the proposed model is nally illustrated by anapplication to a dataset, which derives from a population of Talitrus saltatorfrom the beach of Castiglione della Pescaia (Italy).
Maruotti, A., PUNZO Antonio, M.G., Lagona, F. (2016). A time-dependent extension of the projected Normal regression model for longitudinal circular data based on a hidden Markov heterogeneity structure. STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 30, 1725-1740 [10.1007/s00477-015-1183-5].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/282156
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