[en] Existing circular and toroidal distributions are mostly symmetric; however, many datasets possess asymmetric patterns. Due to the increasing need for asymmetric distributions in recent times, driven by complex modern datasets, in this chapter a new approach is introduced to generate skewed distributions from symmetric distributions, for modeling both circular and toroidal skewed data. This new family of asymmetric distributions, called generalized skew-symmetric distributions, includes some well-known distributions as special cases, such as the circular models of Umbach and Jammalamadaka (Stat Probab Lett 79:659–663, 2009) [24] and Abe and Pewsey (Stat Pap 52:683–707) [1] and the toroidal model of Ameijeiras-Alonso and Ley (Biostatistics, 2020. https://doi.org/10.1093/biostatistics/kxaa039 ) [2]. General properties of the new models are studied, and we see that the proposed distributions are able to provide wider ranges of skewness as their competitors. To illustrate the practical implementation and usefulness of our new general skewing approach, we compare our models to competitors from the literature on several real datasets.
Bekker, Andriette; Department of Statistics, University of Pretoria, Pretoria, South Africa
Nakhaei Rad, Najmeh; Department of Statistics, University of Pretoria, Pretoria, South Africa ; Department of Mathematics and Statistics, Mashhad Branch, Islamic Azad University, Mashhad, Iran ; DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), Johannesburg, South Africa
Arashi, Mohammad; Department of Statistics, University of Pretoria, Pretoria, South Africa ; Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
LEY, Christophe ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) ; Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
External co-authors :
yes
Language :
English
Title :
Generalized Skew-Symmetric Circular and Toroidal Distributions
Acknowledgements Christophe Ley’s research is supported by the FWO Krediet aan Navorsers grant with reference number 1510391N. This work was further based upon research supported in part by the National Research Foundation (NRF) of South Africa, SARChI Research Chair UID: 71199; Ref.: IFR170227223754 grant No. 109214; Ref.: SRUG190308422768 Grant No. 120839, and DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences (CoE-MaSS), South Africa. The opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the CoE-MaSS and the NRF. We would like to thank the reviewers for their thoughtful comments and efforts toward improving our manuscript.
Ameijeiras-Alonso, J., Ley, C.: Sine-skewed toroidal distributions and their application in protein bioinformatics. Biostatistics (2020). https://doi.org/10.1093/biostatistics/kxaa039
Ameijeiras-Alonso, J., Ley, C., Pewsey, A., Verdebout, T.: On optimal tests for circular reflective symmetry about an unknown central direction. Stat. Pap. (2019). https://doi.org/10.1007/s00362-019-01150-7
Ardia, D., Mullen, K., Peterson, B., Ulrich, J., Boudt, K.: DEoptim: global optimization by differential evolution. R package version 2.2-5 (2020)
Azzalini, A.: A class of distributions which includes the normal ones. Scand. J. Stat. 12, 171– 178 (1985)
Azzalini, A., Capitanio, A.: Distributions generated by perturbation of symmetry with emphasis on a multivariate skew-t distribution. J. Roy. Stat. Soc.: Ser. B 65, 367–389 (2003)
Azzalini, A., Capitanio, A.: The Skew-Normal and Related Families. IMS Monographs, Cambridge University Press, Cambridge (2014)
Balakrishnan, N.: Discussion on “Skew multivariate models related to hidden truncation and/or selective reporting” by B. C. Arnold and R. J. Beaver. Test 11, 37–39 (2002)
Buttarazzi, D., Pandolfo, G., Porzio, G.C.: A boxplot for circular data. Biometrics 74(4), 1492– 1501 (2018)
Jander, R.: Die optische Richtungsorientierung der Roten Waldameise (Formica ruea L.). Zeitschrift für vergleichende Physiologie, 40(2), 162–238 (1957)
Jupp, P.E., Regoli, G., Azzalini, A.: A general setting for symmetric distributions and their relationship to general distributions. J. Multivariate Anal. 148, 107–119 (2016)
Ley, C., Babić, S., Craens, D.: Flexible models for complex data with applications. Annu. Rev. Stat. Appl. (2020)
Ley, C., Verdebout, T.: Simple optimal tests for circular reflective symmetry about a specified median direction. Stat. Sinica 24, 1319–1339 (2014)
Ley, C., Verdebout, T.: Skew-rotationally-symmetric distributions and related efficient inferential procedures. J. Multivariate Anal. 159, 67–81 (2017)
Ley, C., Verdebout, T.: Modern Directional Statistics. Chapman and Hall/CRC Press, Boca Raton, Florida (2017)
Liu, D., Peddada, S.D., Li, L., Weinberg, C.R.: Phase analysis of circadian-related genes in two tissues. BMC Bioinf. 7(1), 77–87 (2006)
Mardia, K.V.: Statistics of Directional Data. Academic Press, New York (1972)
Meintanis, S., Verdebout, T.: Le Cam maximin tests for symmetry of circular data based on the characteristic function. Stat. Sinica 29, 1301–1320 (2019)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer Science and Business Media, Berlin (2006)
SenGupta, A., Ong, S.H.: A unified approach for construction of probability models for bivariate linear and directional data. Commun. Stat.-Theory Methods 43, 2563–2569 (2014)
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Umbach, D., Jammalamadaka, S.R.: Building asymmetry into circular distributions. Stat. Probab. Lett. 79(5), 659–663 (2009)