Model-based clustering for noisy longitudinal circular data, with application to animal movement

被引：4
|
作者：
Ranalli, M. [1 ]
Maruotti, A. [2 ,3 ]
机构：
[1] Univ Roma Tor Vergata, Dipartimento Econ & Finanza, Via Columbia 2, I-00133 Rome, Italy
[2] Libera Univ Maria SS Assunta, Dipartimento Giurisprudenza Econ Polit & Lingue M, Rome, Italy
[3] Univ Bergen, Dept Math, Bergen, Norway
来源：
ENVIRONMETRICS | 2020年 / 31卷 / 02期
关键词：
animal movement; hidden Markov models; projected normal distribution; robust clustering; HIDDEN MARKOV-MODELS; ROBUST ESTIMATION; DIRECTIONAL-DATA; LIKELIHOOD; IDENTIFIABILITY; SEGMENTATION; MAXIMIZATION;
D O I：
10.1002/env.2572
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
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.
引用
收藏
页数：17
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