Tukey lambda distribution

Formalized by John Tukey, the Tukey lambda distribution is a continuous, symmetric probability distribution defined in terms of its quantile function. It is typically used to identify an appropriate distribution (see the comments below) and not used in statistical models directly.

The Tukey lambda distribution has a single shape parameter, λ, and as with other probability distributions, it can be transformed with a location parameter, μ, and a scale parameter, σ. Since the general form of probability distribution can be expressed in terms of the standard distribution, the subsequent formulas are given for the standard form of the function.

Quantile function

For the standard form of the Tukey lambda distribution, the quantile function, $~Q(p)~,$ (i.e. the inverse function to the cumulative distribution function) and the quantile density function ($~ q = \operatornameQ / \operatornamep ~$ are

:
Q\left(p;\lambda\right) ~=~
\begin
\frac \left ^\lambda - (1 - p)^\lambda\right, & \mbox \lambda \ne 0~, \\
\log(\frac)~, & \mbox \lambda = 0~.
\end

:$q\left(p;\lambda\right) ~=~ \operatornameQ/\operatornamep ~=~ p^ + \left(1-p\right)^~.$

For most values of the shape parameter, , the probability density function (PDF) and cumulative distribution function (CDF) must be computed numerically. The Tukey lambda distribution has a simple, closed form for the CDF and / or PDF only for a few exceptional values of the shape parameter, for example: (see uniform distribution ase = 1and the logistic distribution ase = 0.

However, for any value of both the CDF and PDF can be tabulated for any number of cumulative probabilities, , using the quantile function to calculate the value , for each cumulative probability , with the probability density given by , the reciprocal of the quantile density function. As is the usual case with statistical distributions, the Tukey lambda distribution can readily be used by looking up values in a prepared table.

Moments

The Tukey lambda distribution is symmetric around zero, therefore the expected value of this distribution is equal to zero. The variance exists for and is given by the formula (except when ''λ'' = 0)
:
\operatorname = \frac\bigg(\frac - \frac\bigg).

More generally, the ''n''-th order moment is finite when and is expressed in terms of the beta function ''Β''(''x'',''y'') (except when ''λ'' = 0) :
:
\mu_n = \operatorname ^n= \frac \sum_^n (-1)^k \, \Beta(\lambda k+1,\, \lambda(n-k)+1 ).

Note that due to symmetry of the density function, all moments of odd orders are equal to zero.

L-moments

Differently from the central moments, L-moments can be expressed in a closed form. The L-moment of order ''r>1'' is given by
:$L_ = \frac\sum_^ (-1)^ \binom \binom \left(\frac \right).$
The first six L-moments can be presented as follows:
:$L_=0$
:
L_2 = \frac\left - \frac + \frac \right
:$L_3 =0$
:
L_4 = \frac\left - \frac + \frac - \frac + \frac \right
:$L_5 =0$
:
L_6 = \frac\left -\frac + \frac - \frac +\frac-\frac+\frac \right,.

Comments

The Tukey lambda distribution is actually a family of distributions that can approximate a number of common distributions. For example,

The most common use of this distribution is to generate a Tukey lambda PPCC plot of a data set. Based on the PPCC plot, an appropriate model for the data is suggested. For example, if the best-fit of the curve to the data occurs for a value of at or near 0.14, then the data could be well-modeled with a normal distribution. Values of less than 0.14 suggests a heavier-tailed distribution; a milepost at = 0 (logistic) would indicate quite fat tails, with the extreme limit at = −1, approximating Cauchy. That is, as the best-fit value of varies from 0.14 towards −1, a bell-shaped PDF with increasingly heavy tails is suggested. Similarly, for an optimal value of becomes greater than 0.14 suggests a distribution with ''exceptionally'' thin tails (based on the point of view that the normal distribution itself is thin-tailed to begin with).

Except for values of very close to 0, all the suggested PDF functions have finite support, between     and    .

Since the Tukey lambda distribution is a symmetric distribution, the use of the Tukey lambda PPCC plot to determine a reasonable distribution to model the data only applies to symmetric distributions. A histogram of the data should provide evidence as to whether the data can be reasonably modeled with a symmetric distribution.

References

External links

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{{ProbDistributions, continuous-variable

Continuous distributions
Probability distributions with non-finite variance