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    Fitting a distribution to data using MLE




    I have a dataset to which I'd like to fit a distribution using MLE. In the old days, I would have taken a guess and then used a KS test to see if the fit were good. I've been led to believe that modern (i.e. written in the last 10 years) can do this for me.

    It's been a while since I've actually worked for a living, so please be patient with me.

    The data is more-or-less symmetrical and is leptokurtic. My prior is that a t-distribution with n < 20 might work.

    What package do I want to use and which algorithm within that package? Is there a website into which I can just plug the data to get a guess? What's the best (basic) citation on this topic?

    Thanks.

    Me: 55, MS ms stat (1994), senior manager who likes to get his hands dirty.

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    Re: Fitting a distribution to data using MLE

    Hi,
    I generally use Minitab which has a distribution identification menupoint with a good choice of the most frequently seen distributions. Just searching around I found this shareware: http://www.variation.com/da/

    If t seems to be a good choice maybe you could try the Weibull distribution - the universal workhorse of distribution fits

    regards

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    Re: Fitting a distribution to data using MLE

    Thanks much. Minitab. Wow. I used that in college in the 80s.

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    Re: Fitting a distribution to data using MLE

    version 17 is pretty neat

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    Re: Fitting a distribution to data using MLE


    If you are using R then the package gamlss kan be useful. (site: gamlss.org ) Then you can try 70 distributions.

    R is the standard in statistics nowadays. R and RStudio is free.

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