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Re: Re: st: Automatic fit of distribution
From
David Hoaglin <[email protected]>
To
[email protected]
Subject
Re: Re: st: Automatic fit of distribution
Date
Thu, 11 Jul 2013 12:29:15 -0400
Diagnostics are fine, but there is no sustitute for looking at the
data (e.g., in well-chosen histograms and quantile-quantile plots).
Programs that rely on the sample skewness and kurtosis will be blind
to mixtures that show more than one mode, and the sensitivity of
sample moments to outliers makes those measures unsuitable for
diagnosing distribution shape.
Also, the process should take into account whether the data are
continuous or discrete.
David Hoaglin
On Thu, Jul 11, 2013 at 11:45 AM, Ariel Linden. DrPH
<[email protected]> wrote:
> I completely agree with Nick and Maarten that the user should do the work
> required to determine what type of distribution they are dealing with and go
> from there. However, it seems to me that there could be a program that
> "points the user in the right direction" after running a few simple
> diagnostics. For example, there are several programs already available to
> test for normality (ie., -sktest-, -swilk-, -ksmirnov-). It would be rather
> straightforward to test for a Poisson distribution based on the variance =
> mean. It would get harder as we go to other distributions, or fall between
> choices...
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