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A tractable, parsimonious and flexible model for cylindrical data, with applications

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  • Abe, Toshihiro
  • Ley, Christophe

Abstract

New cylindrical distributions are proposed by combining the sine-skewed von Mises distribution (circular part) with the Weibull distribution (linear part). This new model, the WeiSSVM, enjoys numerous advantages: simple normalizing constant and hence very tractable density, parameter-parsimony and interpretability, good circular–linear dependence structure, easy random number generation thanks to known marginal/conditional distributions, and flexibility illustrated via excellent fitting abilities. Inferential issues, such as independence testing, circular–linear respectively linear–circular regression, can easily be tackled with the new model, which is applied on two real data sets.

Suggested Citation

  • Abe, Toshihiro & Ley, Christophe, 2017. "A tractable, parsimonious and flexible model for cylindrical data, with applications," Econometrics and Statistics, Elsevier, vol. 4(C), pages 91-104.
    • Handle: RePEc:eee:ecosta:v:4:y:2017:i:c:p:91-104
    DOI: 10.1016/j.ecosta.2016.04.001
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    References listed on IDEAS

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    1. Francesco Lagona & Marco Picone & Antonello Maruotti, 2015. "A hidden Markov model for the analysis of cylindrical time series," Environmetrics, John Wiley & Sons, Ltd., vol. 26(8), pages 534-544, December.
    2. Toshihiro Abe & Arthur Pewsey, 2011. "Sine-skewed circular distributions," Statistical Papers, Springer, vol. 52(3), pages 683-707, August.
    3. Jones, M.C. & Pewsey, Arthur, 2005. "A Family of Symmetric Distributions on the Circle," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1422-1428, December.
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    Cited by:

    1. Mastrantonio, Gianluca, 2018. "The joint projected normal and skew-normal: A distribution for poly-cylindrical data," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 14-26.
    2. Arthur Pewsey & Eduardo García-Portugués, 2021. "Recent advances in directional statistics," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(1), pages 1-58, March.
    3. Joseph D. Bailey & Edward A. Codling, 2021. "Emergence of the wrapped Cauchy distribution in mixed directional data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 229-246, June.
    4. Andrew Harvey & Dario Palumbo, 2023. "Regime switching models for circular and linear time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(4), pages 374-392, July.
    5. Andrade, Ana C.C. & Pereira, Gustavo H.A. & Artes, Rinaldo, 2023. "The circular quantile residual," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    6. Yoichi Miyata & Takayuki Shiohama & Toshihiro Abe, 2023. "Identifiability of Asymmetric Circular and Cylindrical Distributions," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(2), pages 1431-1451, August.
    7. Marc Hallin & H Lui & Thomas Verdebout, 2022. "Nonparametric Measure-transportation-based Methods for Directional Data," Working Papers ECARES 2022-18, ULB -- Universite Libre de Bruxelles.
    8. Imoto, Tomoaki & Abe, Toshihiro, 2021. "Simple construction of a toroidal distribution from independent circular distributions," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    9. Ghosh, Malay & Zhong, Xiaolong & SenGupta, Ashis & Zhang, Ruoyang, 2019. "Non-subjective priors for wrapped Cauchy distributions," Statistics & Probability Letters, Elsevier, vol. 153(C), pages 90-97.
    10. Gugliani, G.K. & Sarkar, A. & Ley, C. & Mandal, S., 2018. "New methods to assess wind resources in terms of wind speed, load, power and direction," Renewable Energy, Elsevier, vol. 129(PA), pages 168-182.

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