Statistical modeling of directional data using a robust hierarchical von mises distribution model: perspectives for wind energy

Said Benlakhdar (), Mohammed Rziza and Rachid Oulad Haj Thami
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Said Benlakhdar: Mohammed V University in Rabat
Mohammed Rziza: Mohammed V University in Rabat
Rachid Oulad Haj Thami: Mohammed V University in Rabat

Computational Statistics, 2022, vol. 37, issue 4, No 3, 1599-1619

Abstract: Abstract For describing wind direction, a variety of statistical distributions has been suggested that provides information about the wind regime at a particular location and aids the development of efficient wind energy generation. In this paper a systematic approach for data classification putting a special emphasis on the von Mises mixtures is presented. A von Mises mixture model is broad enough to cover, on one hand, symmetry and asymmetry, on the other hand, unimodality and multimodality of circular data. We developed an improved mathematical model of the classical von Mises mixture method, rests on number of principles which gives its internal coherence and originality. In principle, our hierarchical model of von Mises distributions is flexible to precisely modeled complex directional data sets. We define a new specific expectation–maximization (S-EM) algorithm for estimating the parameters of the model. The simulation showed that satisfactory fit of complex directional data could be obtained (error generally

Keywords: Hierarchical structure; Mixture model; Kernel density estimation; von Mises distribution; Wind energy (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01173-5

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