Modeling Joint Cylindrical Distributions and Related Markov Processes

Toshihiro Abe (), Tomoaki Imoto, Takayuki Shiohama () and Yoichi Miyata ()
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Toshihiro Abe: Hosei University, Faculty of Economics
Tomoaki Imoto: University of Shizuoka, School of Management and Information
Takayuki Shiohama: Nanzan University, Department of Data Science
Yoichi Miyata: Takasaki City University of Economics, Faculty of Economics

A chapter in Directional and Multivariate Statistics, 2025, pp 103-129 from Springer

Abstract: Abstract Joint cylindrical distributions are key probability distributions that make possible a multivariate regression analysis as well as Markov models on a cylinder. In this study, some joint cylindrical distributions are proposed, and their statistical properties together with algorithms for random number generation are investigated. For proposed joint cylindrical distributions, the marginal distributions for various combinations of linear and circular variables are obtained. These marginal distributions are applied to the Markov process on a cylinder. The maximum likelihood estimation for unknown model parameters is investigated. To illustrate the applicability of the proposed models, time series analysis using wind speed and direction data is investigated.

Keywords: Circular statistics; Cylindrical data; Markov process; Maximum likelihood estimation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-2004-3_6

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DOI: 10.1007/978-981-96-2004-3_6

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