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The generalized gamma distribution has the ability to mimic the attributes of other distributions such as the Weibull or lognormal, based on the values of the distribution's parameters. While the generalized gamma distribution is not often used to model life data by itself (mostly due to its mathematical complexity and its requirement of large sample sizes (>30) for convergence), its ability to behave like other more commonly-used life distributions is sometimes used to determine which of those life distributions should be used to model a particular set of data.
The generalized gamma distribution has the ability to mimic the attributes of other distributions, such as Weibull or lognormal, based on the values of the distribution's parameters. While the generalized gamma distribution is not often used to model life data by itself (mostly due to its mathematical complexity and its requirement of large sample sizes >30 for convergence), its ability to behave like other more commonly-used life distributions is sometimes used to determine which of those life distributions should be used to model a particular set of data.
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Latest revision as of 21:28, 17 April 2015

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Generalized Gamma Distibution

The generalized gamma distribution has the ability to mimic the attributes of other distributions, such as Weibull or lognormal, based on the values of the distribution's parameters. While the generalized gamma distribution is not often used to model life data by itself (mostly due to its mathematical complexity and its requirement of large sample sizes >30 for convergence), its ability to behave like other more commonly-used life distributions is sometimes used to determine which of those life distributions should be used to model a particular set of data.


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