New power-law tailed distributions emerging in κ-statistics

03/03/2022
by   G. Kaniadakis, et al.
0

Over the last two decades, it has been argued that the Lorentz transformation mechanism, which imposes the generalization of Newton's classical mechanics into Einstein's special relativity, implies a generalization, or deformation, of the ordinary statistical mechanics. The exponential function, which defines the Boltzmann's factor, emerges properly deformed within this formalism. Starting from this, so-called κ-deformed exponential function, we introduce new classes of statistical distributions emerging as the κ-deformed version of already known distribution as the Generalized Gamma, Weibull, Logistic which can be adopted in the analysis of statistical data that exhibit power-law tails.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/19/2022

Development of information system suited for statistical analysis of global brands distributions

This qualification work studies methods of statistical analysis of globa...
research
04/09/2012

On Power-law Kernels, corresponding Reproducing Kernel Hilbert Space and Applications

The role of kernels is central to machine learning. Motivated by the imp...
research
12/20/2020

An Interpolating Family of size distributions

Power laws and power laws with exponential cut-off are two distinct fami...
research
03/31/2019

Distribution of scientific journals impact factor

We consider distributions of scientific journals impact factor. Analysin...
research
03/30/2020

Empirical Analysis of Zipf's Law, Power Law, and Lognormal Distributions in Medical Discharge Reports

Bayesian modelling and statistical text analysis rely on informed probab...
research
09/20/2020

Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Manifold

We provide an Information-Geometric formulation of Classical Mechanics o...
research
01/20/2022

Drones Practicing Mechanics

Mechanics of materials is a classic course of engineering presenting the...

Please sign up or login with your details

Forgot password? Click here to reset