ⓘ Beta rectangular distribution
In probability theory and statistics, the beta rectangular distribution is a probability distribution that is a finite mixture distribution of the beta distribution and the continuous uniform distribution. The support is of the distribution is indicated by the parameters a and b, which are the minimum and maximum values respectively. The distribution provides an alternative to the beta distribution such that it allows more density to be placed at the extremes of the bounded interval of support. Thus it is a bounded distribution that allows for outliers to have a greater chance of occurring than does the beta distribution.
1.1. Definition Probability density function
If parameters of the beta distribution are α and β, and if the mixture parameter is θ, then the beta rectangular distribution has probability density function
p x  α, β, θ = { θ Γ α + β Γ α Γ β x − a α − 1 b − x β − 1 b − a α + β + 1 + 1 − θ b − a f o r a ≤ x ≤ b, 0 f o r x < a o r x > b {\displaystyle px\alpha,\beta,\theta={\begin{cases}{\frac {\theta \Gamma \alpha +\beta}{\Gamma \alpha\Gamma \beta}}{\frac {xa^{\alpha 1}bx^{\beta 1}}{ba^{\alpha +\beta +1}}}+{\frac {1\theta }{ba}}&\mathrm {for} \ a\leq x\leq b,\\0&\mathrm {for} \ x b\end{cases}}}where Γ ⋅ {\displaystyle \Gamma \cdot} is the gamma function.
1.2. Definition Cumulative distribution function
The cumulative distribution function is
F x  α, β, θ = θ I z α, β + 1 − θ x − a b − a f o r a ≤ x ≤ b, {\displaystyle Fx\alpha,\beta,\theta=\theta I_{z}\alpha,\beta+{\frac {1\thetaxa}{ba}}\\quad \mathrm {for} \ a\leq x\leq b,}where z = x − a b − a {\displaystyle z={\dfrac {xa}{ba}}} and I z α, β {\displaystyle I_{z}\alpha,\beta} is the regularized incomplete beta function.
2.1. Applications Project management
The PERT distribution variation of the beta distribution is frequently used in PERT, critical path method CPM and other project management methodologies to characterize the distribution of an activitys time to completion.
In PERT, restrictions on the PERT distribution parameters lead to shorthand computations for the mean and standard deviation of the beta distribution:
E x = a + 4 m + b 6 Var x = b − a 2 36 {\displaystyle {\begin{aligned}Ex&{}={\frac {a+4m+b}{6}}\\\operatorname {Var} x&{}={\frac {ba^{2}}{36}}\end{aligned}}}where a is the minimum, b is the maximum, and m is the mode or most likely value. However, the variance is seen to be a constant conditional on the range. As a result, there is no scope for expressing differing levels of uncertainty that the project manager might have about the activity time.
Eliciting the beta rectangulars certainty parameter θ allows the project manager to incorporate the rectangular distribution and increase uncertainty by specifying θ is less than 1. The above expectation formula then becomes
E x = θ a + 4 m + b + 3 1 − θ a + b 6. {\displaystyle Ex={\frac {\theta a+4m+b+31\thetaa+b}{6}}.}If the project manager assumes the beta distribution is symmetric under the standard PERT conditions then the variance is
Var x = b − a 2 3 − 2 θ 36, {\displaystyle \operatorname {Var} x={\frac {ba^{2}32\theta}{36}},}while for the asymmetric case it is
Var x = b − a 2 3 − 2 θ 2 36. {\displaystyle \operatorname {Var} x={\frac {ba^{2}32\theta ^{2}}{36}}.}The variance can now be increased when uncertainty is larger. However, the beta distribution may still apply depending on the project managers judgment.
The beta rectangular has been compared to the uniformtwo sided power distribution and the uniformgeneralized biparabolic distribution in the context of project management. The beta rectangular exhibited larger variance and smaller kurtosis by comparison.
2.2. Applications Income distributions
The beta rectangular distribution has been compared to the elevated twosided power distribution in fitting U.S. income data. The 5parameter elevated twosided power distribution was found to have a better fit for some subpopulations, while the 3parameter beta rectangular was found to have a better fit for other subpopulations.
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