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

• Published in: Mathematics (Basel) Vol. 7; no. 5; p. 403
• Main Authors: Contreras-Reyes, Javier E., Maleki, Mohsen, Devia Cortés, Daniel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2019
• Subjects: Algorithms; Asymptotic methods; Entropy; Gompertz distribution; Hypotheses; Kullback–Leibler divergence; Random variables; Sea surface temperature; Skew-Reflected-Gompertz distribution; Skewed distributions
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math7050403

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.