Template:Generalized gamma probability density function: Difference between revisions
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where <math>\theta >0</math> is a scale parameter, <math>\beta >0</math> and <math>k>0</math> are shape parameters and <math>\Gamma (x)</math> is the gamma function of ''x'', which is defined by: | where <math>\theta >0</math> is a scale parameter, <math>\beta >0</math> and <math>k>0</math> are shape parameters and <math>\Gamma (x)</math> is the gamma function of ''x'', which is defined by: | ||
::<math>\Gamma (x)=\int_{0}^{\infty }{{{s}^{x-1}}}\cdot {{e}^{-s}}ds</math> | |||
With this version of the distribution, however, convergence problems arise that severely limit its usefulness. Even with data sets containing 200 or more data points, the MLE methods may fail to converge. Further adding to the confusion is the fact that distributions with widely different values of , , and | With this version of the distribution, however, convergence problems arise that severely limit its usefulness. Even with data sets containing 200 or more data points, the MLE methods may fail to converge. Further adding to the confusion is the fact that distributions with widely different values of ''k'', <math>\beta</math>, and <math>\theta </math> may appear almost identical [[Appendix: Weibull References|[21]]]. In order to overcome these difficulties, Weibull++ uses a reparameterization with parameters ''k'', <math>\beta</math>, and <math>\theta </math> [[Appendix: Weibull References|[21]]] where: | ||
where and While this makes the distribution converge much more easily in computations, it does not facilitate manual manipulation of the equation. By allowing to become negative, the of the reparameterized distribution is given by: | where and While this makes the distribution converge much more easily in computations, it does not facilitate manual manipulation of the equation. By allowing to become negative, the of the reparameterized distribution is given by: | ||
Revision as of 19:06, 14 February 2012
Generalized Gamma Probability Density Function
The generalized gamma function is a three-parameter distribution. One version of the generalized gamma distribution uses the parameters k, [math]\displaystyle{ \beta }[/math], and [math]\displaystyle{ \theta }[/math]. The pdf for this form of the generalized gamma distribution is given by:
- [math]\displaystyle{ f(t)=\frac{\beta }{\Gamma (k)\cdot \theta }{{\left( \frac{t}{\theta } \right)}^{k\beta -1}}{{e}^{-{{\left( \frac{t}{\theta } \right)}^{\beta }}}} }[/math]
where [math]\displaystyle{ \theta \gt 0 }[/math] is a scale parameter, [math]\displaystyle{ \beta \gt 0 }[/math] and [math]\displaystyle{ k\gt 0 }[/math] are shape parameters and [math]\displaystyle{ \Gamma (x) }[/math] is the gamma function of x, which is defined by:
- [math]\displaystyle{ \Gamma (x)=\int_{0}^{\infty }{{{s}^{x-1}}}\cdot {{e}^{-s}}ds }[/math]
With this version of the distribution, however, convergence problems arise that severely limit its usefulness. Even with data sets containing 200 or more data points, the MLE methods may fail to converge. Further adding to the confusion is the fact that distributions with widely different values of k, [math]\displaystyle{ \beta }[/math], and [math]\displaystyle{ \theta }[/math] may appear almost identical [21]. In order to overcome these difficulties, Weibull++ uses a reparameterization with parameters k, [math]\displaystyle{ \beta }[/math], and [math]\displaystyle{ \theta }[/math] [21] where:
where and While this makes the distribution converge much more easily in computations, it does not facilitate manual manipulation of the equation. By allowing to become negative, the of the reparameterized distribution is given by: