Beta prime distribution
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In probability theory and statistics, the beta prime distribution (also known as inverted beta distribution or beta distribution of the second kind[1]) is an absolutely continuous probability distribution. If has a beta distribution, then the odds has a beta prime distribution.
Probability density function ![]() | |||
Cumulative distribution function ![]() | |||
Parameters |
shape (real) shape (real) | ||
---|---|---|---|
Support | |||
CDF | where is the regularized incomplete beta function | ||
Mean | if | ||
Mode | |||
Variance | if | ||
Skewness | if | ||
Excess kurtosis | if | ||
Entropy | where is the digamma function. | ||
MGF | Does not exist | ||
CF |
Definitions
Summarize
Perspective
Beta prime distribution is defined for with two parameters α and β, having the probability density function:
where B is the Beta function.
The cumulative distribution function is
where I is the regularized incomplete beta function.
While the related beta distribution is the conjugate prior distribution of the parameter of a Bernoulli distribution expressed as a probability, the beta prime distribution is the conjugate prior distribution of the parameter of a Bernoulli distribution expressed in odds. The distribution is a Pearson type VI distribution.[1]
The mode of a variate X distributed as is . Its mean is if (if the mean is infinite, in other words it has no well defined mean) and its variance is if .
For , the k-th moment is given by
For with this simplifies to
The cdf can also be written as
where is the Gauss's hypergeometric function 2F1 .
Alternative parameterization
The beta prime distribution may also be reparameterized in terms of its mean μ > 0 and precision ν > 0 parameters ([2] p. 36).
Consider the parameterization μ = α/(β − 1) and ν = β − 2, i.e., α = μ(1 + ν) and β = 2 + ν. Under this parameterization E[Y] = μ and Var[Y] = μ(1 + μ)/ν.
Generalization
Two more parameters can be added to form the generalized beta prime distribution :
having the probability density function:
with mean
and mode
Note that if p = q = 1 then the generalized beta prime distribution reduces to the standard beta prime distribution.
This generalization can be obtained via the following invertible transformation. If and for , then .
Compound gamma distribution
The compound gamma distribution[3] is the generalization of the beta prime when the scale parameter, q is added, but where p = 1. It is so named because it is formed by compounding two gamma distributions:
where is the gamma pdf with shape and inverse scale .
The mode, mean and variance of the compound gamma can be obtained by multiplying the mode and mean in the above infobox by q and the variance by q2.
Another way to express the compounding is if and , then . This gives one way to generate random variates with compound gamma, or beta prime distributions. Another is via the ratio of independent gamma variates, as shown below.
Properties
- If then .
- If , and , then .
- If then .
Related distributions
- If , then . This property can be used to generate beta prime distributed variates.
- If , then . This is a corollary from the property above.
- If has an F-distribution, then , or equivalently, .
- For gamma distribution parametrization I:
- If are independent, then . Note are all scale parameters for their respective distributions.
- For gamma distribution parametrization II:
- If are independent, then . The are rate parameters, while is a scale parameter.
- If and , then . The are rate parameters for the gamma distributions, but is the scale parameter for the beta prime.
- the Dagum distribution
- the Singh–Maddala distribution.
- the log logistic distribution.
- The beta prime distribution is a special case of the type 6 Pearson distribution.
- If X has a Pareto distribution with minimum and shape parameter , then .
- If X has a Lomax distribution, also known as a Pareto Type II distribution, with shape parameter and scale parameter , then .
- If X has a standard Pareto Type IV distribution with shape parameter and inequality parameter , then , or equivalently, .
- The inverted Dirichlet distribution is a generalization of the beta prime distribution.
- If , then has a generalized logistic distribution. More generally, if , then has a scaled and shifted generalized logistic distribution.
- If , then follows a Cauchy distribution, which is equivalent to a student-t distribution with the degrees of freedom of 1.
Notes
References
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