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Multivariate Birnbaum-Saunders Distributions: Modelling and Applications

by Robert G. Aykroyd 1,*,†, Víctor Leiva 2,† and Carolina Marchant 3,†
1
Department of Statistics, University of Leeds, Leeds LS2 9JT, UK
2
School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
3
Faculty of Basic Sciences, Universidad Católica del Maule, Talca 3480112, Chile
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Risks 2018, 6(1), 21; https://doi.org/10.3390/risks6010021
Received: 21 January 2018 / Revised: 19 February 2018 / Accepted: 22 February 2018 / Published: 8 March 2018
Since its origins and numerous applications in material science, the Birnbaum–Saunders family of distributions has now found widespread uses in some areas of the applied sciences such as agriculture, environment and medicine, as well as in quality control, among others. It is able to model varied data behaviour and hence provides a flexible alternative to the most usual distributions. The family includes Birnbaum–Saunders and log-Birnbaum–Saunders distributions in univariate and multivariate versions. There are now well-developed methods for estimation and diagnostics that allow in-depth analyses. This paper gives a detailed review of existing methods and of relevant literature, introducing properties and theoretical results in a systematic way. To emphasise the range of suitable applications, full analyses are included of examples based on regression and diagnostics in material science, spatial data modelling in agricultural engineering and control charts for environmental monitoring. However, potential future uses in new areas such as business, economics, finance and insurance are also discussed. This work is presented to provide a full tool-kit of novel statistical models and methods to encourage other researchers to implement them in these new areas. It is expected that the methods will have the same positive impact in the new areas as they have had elsewhere. View Full-Text
Keywords: asymmetric distributions; control charts; diagnostics; multivariate methods; non-normality; regression; R software; spatial models asymmetric distributions; control charts; diagnostics; multivariate methods; non-normality; regression; R software; spatial models
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Aykroyd, R.G.; Leiva, V.; Marchant, C. Multivariate Birnbaum-Saunders Distributions: Modelling and Applications. Risks 2018, 6, 21.

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