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Re: st: Nonparametric Methods for Longitudinal Data


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Nonparametric Methods for Longitudinal Data
Date   Mon, 11 Feb 2013 14:13:44 +0000

That's a Memorable Mnemonic.

Roger did not mention that one merit of doing this kind of statistics
the way in his style is that he focuses on quantifiable parameters,
usually relatives of p(X > Y | conditions) for some X, Y, conditions.

Nick

On Mon, Feb 11, 2013 at 2:06 PM, Roger B. Newson
<r.newson@imperial.ac.uk> wrote:
> I would argue that there are no such things as "non-parametric statistics".
> There are rank methods and spline methods, and both are frequently called
> "non-parametric", but both are actually based on parameters, which can be
> estimated with confidence limits (not just P-values).
>
> My usual mnemonic (for teaching first-year medical students) is that
> measurements on indiViduals are called Variables, measurements on
> Populations are called Parameters, and measurements on Samples are called
> Statistics. So, we use Variables measured on indiViduals to calculate
> Statistics on Samples, which we use to estimate Parameters in the
> corresponding Population. And these parameters may be rank or spline
> parameters.
>
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