Kaniadakis Weibull distribution

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Template:Short descriptionTemplate:NotabilityTemplate:Infobox probability distribution

The Kaniadakis Weibull distribution (or κ-Weibull distribution) is a probability distribution arising as a generalization of the Weibull distribution.[1][2] It is one example of a Kaniadakis κ-distribution. The κ-Weibull distribution has been adopted successfully for describing a wide variety of complex systems in seismology, economy, epidemiology, among many others.

Definitions

Probability density function

The Kaniadakis κ-Weibull distribution is exhibits power-law right tails, and it has the following probability density function:[3]

fκ(x)=αβxα−11+κ2β2x2αexpκ(−βxα)

valid for x≥0, where |κ|<1 is the entropic index associated with the Kaniadakis entropy, β>0 is the scale parameter, and α>0 is the shape parameter or Weibull modulus.

The Weibull distribution is recovered as κ→0.

Cumulative distribution function

The cumulative distribution function of κ-Weibull distribution is given by

Fκ(x)=1−expκ(−βxα)

valid for x≥0. The cumulative Weibull distribution is recovered in the classical limit κ→0.

Survival distribution and hazard functions

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

Sκ(x)=expκ(−βxα)

valid for x≥0. The survival Weibull distribution is recovered in the classical limit κ→0.

Comparison between the Kaniadakis κ-Weibull probability function and its cumulative.

The hazard function of the κ-Weibull distribution is obtained through the solution of the κ-rate equation:

Sκ(x)dx=−hκSκ(x)

with Sκ(0)=1, where hκ is the hazard function:

hκ=αβxα−11+κ2β2x2α

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

Sκ=e−Hκ(x)

where

Hκ(x)=∫0xhκ(z)dz
Hκ(x)=1κarcsinh(κβxα)

is the cumulative κ-hazard function. The cumulative hazard function of the Weibull distribution is recovered in the classical limit κ→0: H(x)=βxα .

Properties

Moments, median and mode

The κ-Weibull distribution has moment of order m∈ℕ given by

E⁡[Xm]=|2κβ|−m/α1+κmαΓ(12κ−m2α)Γ(12κ+m2α)Γ(1+mα)

The median and the mode are:

xmedian(Fκ)=β−1/α(lnκ(2))1/α
xmode=β−1/α(α2+2κ2(α−1)2κ2(α2−κ2))1/2α(1+4κ2(α2−κ2)(α−1)2[α2+2κ2(α−1)]2−1)1/2α(α>1)

Quantiles

The quantiles are given by the following expression

xquantile(Fκ)=β−1/α[lnκ(11−Fκ)]1/α

with 0≤Fκ≤1.

Gini coefficient

The Gini coefficient is:[3]

Gκ=1−α+κα+12κΓ(1κ−12α)Γ(1κ+12α)Γ(12κ+12α)Γ(12κ−12α)

Asymptotic behavior

The κ-Weibull distribution II behaves asymptotically as follows:[3]

limx→+∞fκ(x)∼ακ(2κβ)−1/κx−1−α/κ
limx→0+fκ(x)=αβxα−1

Applications

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

  • In economy, for analyzing personal income models, in order to accurately describing simultaneously the income distribution among the richest part and the great majority of the population.[1][4][5]
  • In seismology, the κ-Weibull represents the statistical distribution of magnitude of the earthquakes distributed across the Earth, generalizing the Gutenberg–Richter law,[6] and the interval distributions of seismic data, modeling extreme-event return intervals.[7][8]
  • In epidemiology, the κ-Weibull distribution presents a universal feature for epidemiological analysis.[9]

See also

References

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