Shifted Log-logistic Distribution
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The shifted log-logistic distribution is a
probability distribution In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of different possible outcomes for an experiment. It is a mathematical description of a random phenomenon i ...
also known as the generalized log-logistic or the three-parameter log-logistic distribution. It has also been called the generalized logistic distribution, but this conflicts with other uses of the term: see
generalized logistic distribution The term generalized logistic distribution is used as the name for several different families of probability distributions. For example, Johnson et al.Johnson, N.L., Kotz, S., Balakrishnan, N. (1995) ''Continuous Univariate Distributions, Volume 2' ...
.


Definition

The shifted log-logistic distribution can be obtained from the
log-logistic distribution In probability and statistics, the log-logistic distribution (known as the Fisk distribution in economics) is a continuous probability distribution for a non-negative random variable. It is used in survival analysis as a parametric model for events ...
by addition of a shift parameter \delta. Thus if X has a log-logistic distribution then X+\delta has a shifted log-logistic distribution. So Y has a shifted log-logistic distribution if \log(Y-\delta) has a logistic distribution. The shift parameter adds a location parameter to the scale and shape parameters of the (unshifted) log-logistic. The properties of this distribution are straightforward to derive from those of the log-logistic distribution. However, an alternative parameterisation, similar to that used for the generalized Pareto distribution and the generalized extreme value distribution, gives more interpretable parameters and also aids their estimation. In this parameterisation, the
cumulative distribution function In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x. Ev ...
(CDF) of the shifted log-logistic distribution is : F(x; \mu,\sigma,\xi) = \frac for 1 + \xi(x-\mu)/\sigma \geqslant 0, where \mu\in\mathbb R is the location parameter, \sigma>0\, the scale parameter and \xi\in\mathbb R the shape parameter. Note that some references use \kappa = - \xi\,\! to parameterise the shape. The
probability density function In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can ...
(PDF) is : f(x; \mu,\sigma,\xi) = \frac , again, for 1 + \xi(x-\mu)/\sigma \geqslant 0. The shape parameter \xi is often restricted to lie in 1,1 when the probability density function is bounded. When , \xi, >1, it has an asymptote at x = \mu - \sigma/\xi. Reversing the sign of \xi reflects the pdf and the cdf about x=\mu..


Related distributions

* When \mu = \sigma/\xi, the shifted log-logistic reduces to the log-logistic distribution. * When \xi → 0, the shifted log-logistic reduces to the logistic distribution. * The shifted log-logistic with shape parameter \xi=1 is the same as the generalized Pareto distribution with shape parameter \xi=1.


Applications

The three-parameter log-logistic distribution is used in
hydrology Hydrology () is the scientific study of the movement, distribution, and management of water on Earth and other planets, including the water cycle, water resources, and environmental watershed sustainability. A practitioner of hydrology is calle ...
for modelling flood frequency.


Alternate parameterization

An alternate parameterization with simpler expressions for the PDF and CDF is as follows. For the shape parameter \alpha, scale parameter \beta and location parameter \gamma, the PDF is given by f(x) = \frac \bigg(\frac \bigg) ^\bigg(1+\bigg(\frac\bigg)^\alpha\bigg)^ The CDF is given by F(x) = \bigg(1+\bigg(\frac\bigg)^\alpha\bigg)^ The mean is \beta \theta \csc(\theta) + \gamma and the variance is \beta^2\theta \csc(2\theta)-\theta \csc^2(\theta)/math>, where \theta = \frac.


References

{{ProbDistributions, continuous-variable Continuous distributions