SkewNormalDistribution

SkewNormalDistribution[μ,σ,α]
represents a skew-normal distribution with shape parameter α, location parameter μ, and scale parameter σ.

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Background & Context
Background & Context

  • SkewNormalDistribution[μ,σ,α] represents a continuous statistical distribution defined and supported over the set of real numbers and parametrized by a positive real number σ (a "scale parameter") and by two real numbers μ and α (a "location parameter" and a "shape parameter" respectively), which together determine the overall behavior of its probability density function (PDF). In general, the PDF of a skew-normal distribution is unimodal with a single "peak" (i.e. a global maximum), though its overall shape (its height, its spread, and the horizontal location of its maximum) is determined by the values of α, μ, and σ. In addition, the tails of the PDF are "thin", in the sense that the PDF decreases exponentially rather than decreasing algebraically for large values of . (This behavior can be made quantitatively precise by analyzing the SurvivalFunction of the distribution.) SkewNormalDistribution is a perhaps-skewed generalization of the normal distribution (NormalDistribution, sometimes referred to as the centralized normal distribution), and the one-parameter form SkewNormalDistribution[α] is equivalent to SkewNormalDistribution[0,1,α] (sometimes called the standard skew-normal distribution).
  • The skew-normal distribution was first introduced in the 1970s in a paper by A. O'Hagan and T. Leonard on Bayesian estimation with uncertainty, though thorough analysis of the distribution remained unpublished until the mid-1980s. Mathematically, the skew-normal distribution models both the largest component in a standardized binormal distribution (BinormalDistribution) and the maximum of two variates distributed according to the same normal distribution (NormalDistribution). The distribution is used somewhat frequently throughout statistics as well, emerging in the study of so-called threshold autoaggressive stochastic processes and in time series analysis. The skew-normal distribution can also be used to model a number of phenomena in fields such as physiology, finance, numerical and applied mathematics, telecommunications, and image analysis.
  • RandomVariate can be used to give one or more machine- or arbitrary-precision (the latter via the WorkingPrecision option) pseudorandom variates from a skew-normal distribution. Distributed[x,SkewNormalDistribution[μ,σ,α]], written more concisely as xSkewNormalDistribution[μ,σ,α], can be used to assert that a random variable x is distributed according to a skew-normal distribution. Such an assertion can then be used in functions such as Probability, NProbability, Expectation, and NExpectation.
  • The probability density and cumulative distribution functions for skew-normal distributions may be given using PDF[SkewNormalDistribution[μ,σ,α],x] and CDF[SkewNormalDistribution[μ,σ,α],x]. The mean, median, variance, raw moments, and central moments may be computed using Mean, Median, Variance, Moment, and CentralMoment, respectively.
  • DistributionFitTest can be used to test if a given dataset is consistent with a skew-normal distribution, EstimatedDistribution to estimate a skew-normal parametric distribution from given data, and FindDistributionParameters to fit data to a skew-normal distribution. ProbabilityPlot can be used to generate a plot of the CDF of given data against the CDF of a symbolic skew-normal distribution, and QuantilePlot to generate a plot of the quantiles of given data against the quantiles of a symbolic skew-normal distribution.
  • TransformedDistribution can be used to represent a transformed skew-normal distribution, CensoredDistribution to represent the distribution of values censored between upper and lower values, and TruncatedDistribution to represent the distribution of values truncated between upper and lower values. CopulaDistribution can be used to build higher-dimensional distributions that contain a skew-normal distribution, and ProductDistribution can be used to compute a joint distribution with independent component distributions involving skew-normal distributions.
  • SkewNormalDistribution is related to a number of other distributions. It is an immediate generalization of NormalDistribution, in the sense that the PDF of SkewNormalDistribution[μ,σ,0] is precisely that of NormalDistribution[μ,σ]. SkewNormalDistribution can also be realized as a transformation (TransformedDistribution) of both NormalDistribution and BinormalDistribution and is a limiting case of HalfNormalDistribution. SkewNormalDistribution satisfies a somewhat-novel identity, in that the PDF of SkewNormalDistribution[0,σ,α] is equivalent to 2PDF[NormalDistribution[0,σ],x] CDF[NormalDistribution[0,σ],α x]. SkewNormalDistribution is also related to NoncentralBetaDistribution, NoncentralFRatioDistribution, NoncentralChiSquareDistribution, LogNormalDistribution, and MultinormalDistribution.

ExamplesExamplesopen allclose all

Basic Examples  (3)Basic Examples  (3)

Probability density function:

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Cumulative distribution function:

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Mean and variance:

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Introduced in 2010
(8.0)
| Updated in 2016
(10.4)
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