Skew-Reflected-Gompertz Information Quantifiers with Application to Sea Surface Temperature Records

Javier E. Contreras-Reyes, Mohsen Maleki and Daniel Devia Cortés
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Javier E. Contreras-Reyes: Departamento de Estadística, Facultad de Ciencias, Universidad del Bío-Bío, Concepción 4081112, Chile
Mohsen Maleki: Department of Statistics, College of Sciences, Shiraz University, Shiraz 71946 85115, Iran
Daniel Devia Cortés: Departamento de Evaluación de Pesquerías, Instituto de Fomento Pesquero, Valparaíso 2361827, Chile

Mathematics, 2019, vol. 7, issue 5, 1-14

Abstract: The Skew-Reflected-Gompertz (SRG) distribution, introduced by Hosseinzadeh et al. (J. Comput. Appl. Math. (2019) 349, 132–141), produces two-piece asymmetric behavior of the Gompertz (GZ) distribution, which extends the positive to a whole dominion by an extra parameter. The SRG distribution also permits a better fit than its well-known classical competitors, namely the skew-normal and epsilon-skew-normal distributions, for data with a high presence of skewness. In this paper, we study information quantifiers such as Shannon and Rényi entropies, and Kullback–Leibler divergence in terms of exact expressions of GZ information measures. We find the asymptotic test useful to compare two SRG-distributed samples. Finally, as a real-world data example, we apply these results to South Pacific sea surface temperature records.

Keywords: Skew-Reflected-Gompertz distribution; Gompertz distribution; entropy; Kullback–Leibler divergence; sea surface temperature (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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