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Back to the forum  Query: 2017-12-11 12:32 CET (UTC+1h)
 

Goodness of fits: one model, different datasets [General Sta­tis­tics]

posted by ElMaestro - Denmark, 2017-10-06 23:01  - Posting: # 17871
Views: 1,192

Hi all,

I have some different datasets that are not large at all, and I am doing some model fits on them and from that I am extracting a correlation measure like r squared etc. For the sake of simplicity let us just assume there in a single model like a linear regression without weights.

I would like to compare apples and pears, or at least the fits of three different datasets A, B and C. These datasets do not have the same number of data points, so I cannot fairly compare e.g. r squared of A vs B vs C directly.

Akaike and Schwarz are presumably not the way to go, I think, as I am not varying the model but the dataset, so to say. Kolmogorov-Smirnoff would potentially be useful if I had a boatload of points, which I don't anyway. I am very poor at explaining what I think I am looking for :crying: but I would call it a "fit likelihood" or "correlation statistic that is sample size corrected" :lookaround:. Google and Wikipedia aren't my friends in this regard (although on all other matters, including politics, religion, science and baking recipes G. and W. are always providing the right answers).

Does anyone here know of a handy statistic that allows a fair comparison of goodness of fits across datasets with unequal sizes, given a single model??

Muchas gracias.

I could be wrong, but…


Best regards,
ElMaestro

A potentially biased estimator may be a relevant estimator. The case of REML speaks volumes.

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