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Model‐based clustering for noisy longitudinal circular data, with application to animal movement
Author(s) -
Ranalli M.,
Maruotti A.
Publication year - 2020
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.2572
Subject(s) - cluster analysis , hidden markov model , computer science , expectation–maximization algorithm , data set , set (abstract data type) , longitudinal data , markov model , covariate , data mining , markov chain , mathematics , pattern recognition (psychology) , artificial intelligence , algorithm , statistics , machine learning , maximum likelihood , programming language
Abstract In this work, we introduce a model for circular data analysis to robustly estimate parameters, under a longitudinal clustering setting. A hidden Markov model for longitudinal circular data combined with a uniform conditional density on the circle to capture noise observations is proposed. A set of exogenous covariates is available; they are assumed to affect the evolution of clustering over time. Parameter estimation is carried out through a hybrid expectation–maximization algorithm, using recursions widely adopted in the hidden Markov model literature. Examples of application of the proposal on real and simulated data are performed to show the effectiveness of the proposal.