q-log-distributions: Log-concavity and log-convexity

. Tsallis statistics along with Tsallis distributions have been attracting considerable attention of statisticians in recent years, since applications of the q -distributions can be found in various fields. However, only until recently, in 2018, a q -logarithm transformation has been considered in o...

Full description

Saved in:
Bibliographic Details
Published in:European physical journal plus 2018-04, Vol.133 (4), p.163, Article 163
Main Author: Băncescu, Irina
Format: Article
Language:English
Subjects:
Applied and Technical Physics
Atomic
Complex Systems
Concavity
Condensed Matter Physics
Convexity
Entropy
Mathematical and Computational Physics
Molecular
Optical and Plasma Physics
Order quantity
Physics
Physics and Astronomy
Population
Random variables
Regular Article
Scale models
Theoretical
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:. Tsallis statistics along with Tsallis distributions have been attracting considerable attention of statisticians in recent years, since applications of the q -distributions can be found in various fields. However, only until recently, in 2018, a q -logarithm transformation has been considered in order to obtain new Tsallis distributions. In this paper, we introduce the q -log-distributions defined for q > 1 . We propose these models to be considered for modeling demographic data. We discuss the log-concavity and log-convexity of these models with emphasis on log-concavity. We extend the notions of log-concavity and log-convexity. We explore the shapes of the hazard rate functions and give bounds for the mean residual life function.
ISSN:2190-5444
2190-5444
DOI:10.1140/epjp/i2018-12005-3