A Lindley-Type Distribution for Modeling High-Kurtosis Data

Mario A. Rojas and Yuri A. Iriarte
Additional contact information
Mario A. Rojas: Departamento de Matemáticas, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1240000, Chile
Yuri A. Iriarte: Departamento de Matemáticas, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1240000, Chile

Mathematics, 2022, vol. 10, issue 13, 1-19

Abstract: This article proposes a heavy-tailed distribution for modeling positive data. The proposal arises with the ratio of independent random variables, specifically, a Lindley distribution divided by a beta distribution. This leads to a three-parameter extension of the Lindley distribution capable of modeling high levels of kurtosis. The main structural properties of the proposed distribution are derived. The skewness and kurtosis behavior of the distribution are described. Parameter estimation is discussed under consideration of the moment and maximum likelihood methods. Finally, in order to avoid the parameter non-identifiability problem, a two-parameter version of the proposed distribution is derived. The usefulness of this special case is illustrated by fitting data in two real scenarios.

Keywords: extended slash Lindley distribution; kurtosis; Lindley distribution; Lindley slash distribution; maximum likelihood estimator; moment estimator; probability density function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/13/2240/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/13/2240/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:13:p:2240-:d:848141

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().