Estimation of parameters in multivariate wrapped models for data on a p-torus

Anahita Nodehi, Mousa Golalizadeh (), Mehdi Maadooliat and Claudio Agostinelli
Additional contact information
Anahita Nodehi: Tarbiat Modares University
Mousa Golalizadeh: Tarbiat Modares University
Mehdi Maadooliat: Marquette University
Claudio Agostinelli: University of Trento

Computational Statistics, 2021, vol. 36, issue 1, No 8, 193-215

Abstract: Abstract Multivariate circular observations, i.e. points on a torus arise frequently in fields where instruments such as compass, protractor, weather vane, sextant or theodolite are used. Multivariate wrapped models are often appropriate to describe data points scattered on p-dimensional torus. However, the statistical inference based on such models is quite complicated since each contribution in the log-likelihood function involves an infinite sum of indices in $${\mathbb {Z}}^p$$ Z p , where p is the dimension of the data. To overcome this problem, for moderate dimension p, we propose two estimation procedures based on Expectation-Maximisation and Classification Expectation-Maximisation algorithms. We study the performance of the proposed techniques on a Monte Carlo simulation and further illustrate the advantages of the new procedures on three real-world data sets.

Keywords: CEM algorithm; EM algorithm; Estimation procedures; Multivariate wrapped distributions; Torus (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00180-020-01006-x

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