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Inference in log-alpha-power and log-skew-normal multivariate models

Author

Listed:
  • Guillermo Martínez-Flórez
  • Mario Pacheco
  • Ramón Giraldo

Abstract

Random vectors with positive components are common in many applied fields, for example, in meteorology, when daily precipitation is measured through a region Marchenko and Genton (2010). Frequently, the log-normal multivariate distribution is used for modeling this type of data. This modeling approach is not appropriate for data with high asymmetry or kurtosis. Consequently, more flexible multivariate distributions than the log-normal multivariate are required. As an alternative to this distribution, we propose the log-alpha-power multivariate and log-skew-normal multivariate models. The first model is an extension for positive data of the fractional order statistics model Durrans (1992). The second one is an extension of the log-skew-normal model studied by Mateu-Figueras and Pawlowsky-Glahn (2007). We study parameter estimation for these models by means of pseudo-likelihood and maximum likelihood methods. We illustrate the proposal analyzing a real dataset.

Suggested Citation

  • Guillermo Martínez-Flórez & Mario Pacheco & Ramón Giraldo, 2016. "Inference in log-alpha-power and log-skew-normal multivariate models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(15), pages 4397-4415, August.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:15:p:4397-4415
    DOI: 10.1080/03610926.2014.921301
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