Generalized extreme value

  • Grant Cavanaugh

    Grant Cavanaugh - 2012-02-05

    Does anyone have a suggestion for how to fit a generalized extreme value
    distribution in jags?

    In BUGS there is a dgev() function - but I've found it to be very buggy. It
    never seems to run for me.

    I could simply write the pdf of the function into the model deterministically
    (setting the parameters as stochastic nodes). But if I go that route, I don't

    a) are there are draw backs to keep in mind?

    b) how can I deal with the fact that the pdf is discontinuous (i.e. if eta =
    0, then the pdf is represented differently than if it is nonzero)?

    Any suggestions would be hugely appreciated.


  • Tim Handley

    Tim Handley - 2012-02-06

    I'm not familiar with that distribution, but I've had reasonable success
    coding nonincluded distributions using the "ones trick."

    Also, discontinuous or piecewise functions can be implemented using the step
    function. For example, if you have a function with a domain of , and your
    function f(x) has the value g(x) when x=0 and h(x) otherwise, you can code it

    f<- step(-x)g(x) + step(x)h(x)

    You can use more complex combinations of step functions to create more complex
    piecewise probability functions.

  • Grant Cavanaugh

    Grant Cavanaugh - 2012-02-06

    Thanks thandle2.

    I'll give the step function a shot and post the model if/when I get it

    • Nathan Lally

      Nathan Lally - 2014-06-23


      I know it has been a while, but did you ever get that code working? I have a problem where the generalized extreme value distribution would probably be useful.


  • Jan Galkowski

    Jan Galkowski - 2014-10-05

    I wanted to give a shout-out for an implementation of extreme value distributions in JAGS, possibly the dGPD and dGEV like BUGS, but I'd be happy with just an implementation of the Gumbel distribution.

    • Martyn Plummer

      Martyn Plummer - 2014-10-09

      I'll make a note of it.


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