Lomax distribution

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Lomax
Parameters	$\lambda >0$ scale (real) $\alpha > 0$ shape (real)
Support	$x \ge 0$
PDF	${\alpha \over \lambda} \left[{1+ {x \over \lambda}}\right]^{-(\alpha+1)}$
CDF	$1- \left[{1+ {x \over \lambda}}\right]^{-\alpha}$
Mean	${\lambda \over {\alpha -1}} \text{ for } \alpha > 1$ Otherwise undefined
Median	$\lambda (\sqrt[\alpha]{2} - 1)$
Mode	0
Variance	${{\lambda^2 \alpha} \over {(\alpha-1)^2(\alpha-2)}} \text{ for } \alpha > 2$ $\infty \text{ for } 1 < \alpha \le 2$ Otherwise undefined
Skewness	$\frac{2(1+\alpha)}{\alpha-3}\,\sqrt{\frac{\alpha-2}{\alpha}}\text{ for }\alpha>3\,$
Ex. kurtosis	$\frac{6(\alpha^3+\alpha^2-6\alpha-2)}{\alpha(\alpha-3)(\alpha-4)}\text{ for }\alpha>4\,$

The Lomax distribution, also called the Pareto Type II distribution, is a heavy-tail probability distribution often used in business, economics, and actuarial modeling.^[1]^[2] It is named after K. S. Lomax. It is essentially a Pareto distribution that has been shifted so that its support begins at zero.^[3]

Contents

1 Characterization
- 1.1 Probability density function
2 Relation to the Pareto distribution
3 Relation to generalized Pareto distribution
4 Relation to q-exponential distribution
5 See also
6 References

Characterization

Probability density function

The probability density function for the Lomax distribution is given by:

$p(x) = {\alpha \over \lambda} \left[{1+ {x \over \lambda}}\right]^{-(\alpha+1)}, \qquad x \geq 0,$

where shape parameter α>0 and location parameter λ>0. The density can be rewritten in such a way that more clearly shows the relation to the Pareto Type I distribution. That is:

$p(x) = {{\alpha \lambda^\alpha} \over { (x+\lambda)^{\alpha+1}}}.$

Relation to the Pareto distribution

The Lomax distribution is a Pareto Type I distribution shifted so that its support begins at zero. Specifically:

$\text{If } Y \sim \mbox{Pareto}(x_m = \lambda, \alpha), \text{ then } Y - x_m \sim \mbox{Lomax}(\lambda,\alpha).$

The Lomax distribution is a Pareto Type II distribution with x_m=λ and μ=0:

$\text{If } X \sim \mbox{Lomax}(\lambda,\alpha) \text{ then } X \sim \text{P(II)}(x_m = \lambda, \alpha, \mu=0).$

Relation to generalized Pareto distribution

The Lomax distribution is a special case of the generalized Pareto distribution. Specifically:

$\mu = 0,~ \xi = {1 \over \alpha},~ \sigma = {\lambda \over \alpha} .$

Relation to q-exponential distribution

The Lomax distribution is a special case of the q-exponential distribution. The q-exponential extends this distribution to support on a bounded interval. The Lomax parameters are given by:

$\alpha = { {2-q} \over {q-1}}, ~ \lambda = {1 \over \lambda_q (q-1)} .$

References

^ Lomax, K. S. (1954) "Business Failures; Another example of the analysis of failure data". Journal of the American Statistical Association, 49, 847–852. JSTOR 2281544
^ Johnson, N.L., Kotz, S., Balakrishnan, N. (1994) Continuous Univariate Distributions, Volume 1, 2nd Edition, Wiley. ISBN 0-471-58495-9 (pages 575, 602)
^ Van Hauwermeiren M and Vose D (2009). A Compendium of Distributions [ebook]. Vose Software, Ghent, Belgium. Available at www.vosesoftware.com. Accessed 07/07/11

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