Skew normal distribution

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Skew Normal
Probability density function
Probability density plots of skew normal distributions
Cumulative distribution function
Cumulative distribution function plots of skew normal distributions
Parameters \xi \, location (real)
\omega \, scale (positive, real)
\alpha \, shape (real)
Support x \in (-\infty; +\infty)\!
PDF \frac{1}{\omega}\sqrt{\frac{2}{\pi}} e^{-\frac{(x-\xi)^2}{2\omega^2}} \int_{-\infty}^{\alpha\left(\frac{x-\xi}{\omega}\right)}   e^{-\frac{t^2}{2}}\ dt
CDF \Phi\left(\frac{x-\xi}{\omega}\right)-2T\left(\frac{x-\xi}{\omega},\alpha\right)
T(h,a) is Owen's T function
Mean \xi + \omega\delta\sqrt{\frac{2}{\pi}} where \delta = \frac{\alpha}{\sqrt{1+\alpha^2}}
Variance \omega^2\left(1 - \frac{2\delta^2}{\pi}\right)
Skewness \gamma_1 = \frac{4-\pi}{2} \frac{\left(\delta\sqrt{2/\pi}\right)^3}{  \left(1-2\delta^2/\pi\right)^{3/2}}
Ex. kurtosis 2(\pi - 3)\frac{\left(\delta\sqrt{2/\pi}\right)^4}{\left(1-2\delta^2/\pi\right)^2}
MGF M_X\left(t\right)=2\exp\left(\xi t+\frac{\omega^2t^2}{2}\right)\Phi\left(\omega\delta t\right)
CF e^{i t \xi -t^2\omega^2/2}\left(1+i\, \textrm{Erfi}\left(\frac{\delta\omega t}{\sqrt{2}}\right)\right)

In probability theory and statistics, the skew normal distribution is a continuous probability distribution that generalises the normal distribution to allow for non-zero skewness.


Let \phi(x) denote the standard normal probability density function


with the cumulative distribution function given by

\Phi(x) = \int_{-\infty}^{x} \phi(t)\ dt = \frac{1}{2} \left[ 1 + \operatorname{erf} \left(\frac{x}{\sqrt{2}}\right)\right],

where erf is the error function. Then the probability density function (pdf) of the skew-normal distribution with parameter \alpha is given by

f(x) = 2\phi(x)\Phi(\alpha x). \,

This distribution was first introduced by O'Hagan and Leonard (1976).

A stochastic process that underpins the distribution was described by Andel, Netuka and Zvara (1984).[1] Both the distribution and its stochastic process underpinnings were consequences of the symmetry argument developed in Chan and Tong (1986), which applies to multivariate cases beyond normality, e.g. skew multivariate t distribution and others. The distribution is a particular case of a general class of distributions with probability density functions of the form f(x)=2 φ(x) Φ(x) where φ() is any PDF symmetric about zero and Φ() is any CDF whose PDF is symmetric about zero.[2]

To add location and scale parameters to this, one makes the usual transform x\rightarrow\frac{x-\xi}{\omega}. One can verify that the normal distribution is recovered when \alpha = 0, and that the absolute value of the skewness increases as the absolute value of \alpha increases. The distribution is right skewed if \alpha>0 and is left skewed if \alpha<0. The probability density function with location \xi, scale \omega, and parameter \alpha becomes

f(x) = \frac{2}{\omega}\phi\left(\frac{x-\xi}{\omega}\right)\Phi\left(\alpha \left(\frac{x-\xi}{\omega}\right)\right). \,

Note, however, that the skewness of the distribution is limited to the interval (-1,1).


Maximum likelihood estimates for \xi, \omega, and \alpha can be computed numerically, but no closed-form expression for the estimates is available unless \alpha=0. If a closed-form expression is needed, the method of moments can be applied to estimate \alpha from the sample skew, by inverting the skewness equation. This yields the estimate

|\delta| = \sqrt{\frac{\pi}{2} \frac{  |\hat{\gamma}_3|^{\frac{2}{3}}  }{|\hat{\gamma}_3|^{\frac{2}{3}}+((4-\pi)/2)^\frac{2}{3}}}

where \delta = \frac{\alpha}{\sqrt{1+\alpha^2}}, and \hat{\gamma}_3 is the sample skew. The sign of \delta is the same as the sign of \hat{\gamma}_3. Consequently, \hat{\alpha} = \delta/\sqrt{1-\delta^2}.

The maximum (theoretical) skewness is obtained by setting {\delta = 1} in the skewness equation, giving \gamma_3 \approx 0.9952717. However it is possible that the sample skewness is larger, and then \alpha cannot be determined from these equations. When using the method of moments in an automatic fashion, for example to give starting values for maximum likelihood iteration, one should therefore let (for example) |\hat{\gamma}_3| = \min(0.99, |(1/n)\sum{((x_i-\bar{x})/s)^3}|).

Concern has been expressed about the impact of skew normal methods on the reliability of inferences based upon them.[3]

Differential equation[edit]

The differential equation leading to the pdf of the skew normal distribution is

\omega^4 f''(x)+\left(\alpha^2+2\right) \omega^2 (x-\xi)
   f'(x)+f(x) \left(\left(\alpha^2+1\right) (x-\xi )^2+\omega^2\right)=0

with initial conditions

\displaystyle f(0)=\frac{\exp\left(-\frac{\xi^2}{2\omega^2}\right)
   \operatorname{erfc}\left(\frac{\alpha\xi}{\sqrt{2} \omega}\right)}
   {\sqrt{2\pi}\omega} \text{ and} \\[16pt]
\displaystyle f'(0)=\frac{\exp\left(-\frac{\left(\alpha^2+1\right)\xi ^2}
   {2 \omega^2}\right)
   \left(2\alpha\omega+\sqrt{2\pi} \xi
   \exp\left(\frac{\alpha^2 \xi^2}{2 \omega^2}\right)
   \operatorname{erfc}\left(\frac{\alpha\xi}{\sqrt{2} \omega}\right)\right)}

See also[edit]


  • Andel, J., Netuka, I. and Zvara, K. (1984). On threshold autoregressive processes. Kybernetika, 20, 89-106 [1].
  • Chan, K-S. and Tong, H. (1986). A note on certain integral equations associated with non-linear time series analysis. Probability and Related Fields, 73, 153-158.
  • O'Hagan, A. and Leonard, T. (1976). Bayes estimation subject to uncertainty about parameter constraints. Biometrika, 63, 201-202.

External links[edit]