Herman Rubin wrote:
>
> In article <[EMAIL PROTECTED]>,
> Dave <[EMAIL PROTECTED]> wrote:
> >Hi, can anyone give me a definition of what parametric statistical
> >testing is when compared to non-parametric?
>
> If a model is given with a finite number of parameters
> for the underlying distributions and structure, or at
> worst a finite number of parameters to be estimated,
> it is called "parametric". Else, it is misnamed
> "non-parametric"; it should be "infinite parametric"
> as a proper description of what is to be inferred
> involves an infinite number of parameters.
Would you like to give us an example of such a "proper description"?
<grin> Also, surely it would be incorrect to infer a model with more
parameters than one had data.
I know what Herman is getting at, but I don't agree with the details.
The point of a non-parametric model is that one does *not* attempt to
infer parameter values.
One might also ask, what exactly is a parameter? It's usual to suppose
that (for instance) the mean is always a parameter; but is it, if one
doesn't have the rest of a parametric model? Conversely, if one has a
parametric model, the median is usually a valid parameter. (If the
models in the family are symmetric, it is the same as the mean, and the
sample mean and sample median are both valid estimators.)
One might, I think, argue that (for instance) the t test, done on a
sample large enough that one is not concerned about close approximation
to normality, is in fact nonparametric, in that no specific parametric
model is ever assumed.
-Robert Dawson
.
.
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