Kaniadakis logistic distribution

Template:Short descriptionTemplate:NotabilityTemplate:One source Template:Probability distributionThe Kaniadakis Logistic distribution (also known as κ-Logisticdistribution) is a generalized version of the Logistic distribution associated with the Kaniadakis statistics. It is one example of a Kaniadakis distribution. The κ-Logistic probability distribution describes the population kinetics behavior of bosonic ($0<\lambda <1$) or fermionic ($\lambda >1$) character.

Definitions

Probability density function

The Kaniadakis κ-Logistic distribution is a four-parameter family of continuous statistical distributions, which is part of a class of statistical distributions emerging from the Kaniadakis κ-statistics. This distribution has the following probability density function:

$f_{{}_{\kappa }}(x)=\frac{\lambda \alpha \beta x^{\alpha -1}}{\sqrt{1+{\kappa }^2{\beta }^2{x}^{2\alpha }}}\frac{{\exp }_{\kappa }(-\beta x^{\alpha })}{[1+(\lambda -1){\exp }_{\kappa }(-\beta x^{\alpha }){]}^2}$

valid for $x\ge 0$, where $0\le |\kappa |<1$ is the entropic index associated with the Kaniadakis entropy, $\beta >0$ is the rate parameter, $\lambda >0$, and $\alpha >0$ is the shape parameter.

The Logistic distribution is recovered as $\kappa \to 0.$

Cumulative distribution function

The cumulative distribution function of κ-Logistic is given by

$F_{\kappa }(x)=\frac{1-{\exp }_{\kappa }(-\beta x^{\alpha })}{1+(\lambda -1){\exp }_{\kappa }(-\beta x^{\alpha })}$

valid for $x\ge 0$. The cumulative Logistic distribution is recovered in the classical limit $\kappa \to 0$.

Survival and hazard functions

The survival distribution function of κ-Logistic distribution is given by

$S_{\kappa }(x)=\frac{\lambda }{{\exp }_{\kappa }(\beta x^{\alpha })+\lambda -1}$

valid for $x\ge 0$. The survival Logistic distribution is recovered in the classical limit $\kappa \to 0$.

The hazard function associated with the κ-Logistic distribution is obtained by the solution of the following evolution equation:

$\frac{S_{\kappa }(x)}{dx}=-h_{\kappa }{S}_{\kappa }(x)(1-\frac{\lambda -1}{\lambda }{S}_{\kappa }(x))$

with

$S_{\kappa }(0)=1$

, where

$h_{\kappa }$

is the hazard function:

$h_{\kappa }=\frac{\alpha \beta x^{\alpha -1}}{\sqrt{1+{\kappa }^2{\beta }^2{x}^{2\alpha }}}$

The cumulative Kaniadakis κ-Logistic distribution is related to the hazard function by the following expression:

$S_{\kappa }=e^{-H_{\kappa }(x)}$

where $H_{\kappa }(x)=\underset{0}{\overset{x}{\int }}{h}_{\kappa }(z)dz$ is the cumulative hazard function. The cumulative hazard function of the Logistic distribution is recovered in the classical limit $\kappa \to 0$.

Related distributions

• The survival function of the κ-Logistic distribution represents the κ-deformation of the Fermi-Dirac function, and becomes a Fermi-Dirac distribution in the classical limit $\kappa \to 0$.
• The κ-Logistic distribution is a generalization of the κ-Weibull distribution when $\lambda =1$.
• A κ-Logistic distribution corresponds to a Half-Logistic distribution when $\lambda =2$, $\alpha =1$ and $\kappa =0$.
• The ordinary Logistic distribution is a particular case of a κ-Logistic distribution, when $\kappa =0$.

Applications

The κ-Logistic distribution has been applied in several areas, such as:

• In quantum statistics, the survival function of the κ-Logistic distribution represents the most general expression of the Fermi-Dirac function, reducing to the Fermi-Dirac distribution in the limit $\kappa \to 0$.

See also

• Giorgio Kaniadakis
• Kaniadakis statistics
• Kaniadakis distribution
• Kaniadakis κ-Exponential distribution
• Kaniadakis κ-Gaussian distribution
• Kaniadakis κ-Gamma distribution
• Kaniadakis κ-Weibull distribution
• Kaniadakis κ-Erlang distribution

External links

• Kaniadakis Statistics on arXiv.org