DavisDistribution

DavisDistribution
represents a Davis distribution with scale parameter b, shape parameter n, and location parameter μ.

DetailsDetails

  • The probability density for value in a Davis distribution is proportional to for .
  • DavisDistribution allows b to be any positive real number, μ to be any non-negative real number, and .
  • DavisDistribution can be used with such functions as Mean, CDF, and RandomVariate.

Background & Context
Background & Context

  • DavisDistribution[b,n,μ] represents a continuous statistical distribution defined over the interval and parametrized by the values b, n, and μ. Together, the parameters b (a positive real number called the "scale parameter") and n (a positive real number called the "shape parameter") determine the overall height and steepness of the probability distribution function (PDF) of the Davis distribution. The non-negative real "location parameter" μ determines the horizontal location of the PDF. The PDF of the Davis distribution is unimodal and has "fat" tails, in the sense that the PDF decreases algebraically rather than exponentially for large values of .
  • The Davis distribution originated with American mathematician and statistician Harold Davis, who in the 1940s proposed it as an alternative model for income sizes and distributions. The Davis distribution is also a generalization of Planck's law from statistical physics.
  • RandomVariate can be used to give one or more machine- or arbitrary-precision (the latter via the WorkingPrecision option) pseudorandom variates from a Davis distribution. Distributed[x,DavisDistribution[b,n,μ]], written more concisely as , can be used to assert that a random variable x is distributed according to a Davis distribution. Such an assertion can then be used in functions such as Probability, NProbability, Expectation, and NExpectation.
  • The probability distribution and cumulative density functions may be given using PDF[DavisDistribution[b,n,μ],x] and CDF[DavisDistribution[b,n,μ],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 Davis distribution, EstimatedDistribution to estimate a Davis parametric distribution from given data, and FindDistributionParameters to fit data to a Davis distribution. ProbabilityPlot can be used to generate a plot of the CDF of given data against the CDF of a symbolic Davis distribution and QuantilePlot to generate a plot of the quantiles of given data against the quantiles of a symbolic Davis distribution.
  • TransformedDistribution can be used to represent a transformed Davis 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 Davis distribution, and ProductDistribution can be used to compute a joint distribution with independent component distributions involving Davis distributions.
  • DavisDistribution is related to a number of other distributions. For example, the long-term decay of DavisDistribution is asymptotic to that of ParetoDistribution, consistent with it's having fat tails. Qualitatively, DavisDistribution is related to a number of other distributions that are also used to model income, including BetaPrimeDistribution, DagumDistribution, LogLogisticDistribution, and BeniniDistribution. DavisDistribution is also related to BenktanderGibratDistribution and BenktanderWeibullDistribution.
Introduced in 2010
(8.0)
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