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11-21-2013 12:19 PM

Hi All, Is there a simple explanation why AICC reported by GLIMMIX when doing a simple regression changes with the scale of the regressor :

**data test;****call streaminit(756623);****do x = 1 to 20;**** x1000 = x * 1000;**** x0001 = x * 0.001;**** y = 2 * x + rand("NORMAL");**** output;**** end;****run;**

** **

**proc glimmix data=test;****model y = x0001;****ods output FitStatistics=Fit_0001;****run;**

** **

**proc glimmix data=test;****model y = x;****ods output FitStatistics=Fit_1;****run;**

** **

**proc glimmix data=test;****model y = x1000;****ods output FitStatistics=Fit_1000;****run;**

** **

**data FSall;****set Fit_0001 Fit_1 Fit_1000 indsname=source;****where descr =: "AICC";****from = source;****run;**

** **

**proc print data=FSall noobs; run;**

** Descr Value from**

** AICC (smaller is better) 58.17 WORK.FIT_0001**** AICC (smaller is better) 71.99 WORK.FIT_1**** AICC (smaller is better) 85.80 WORK.FIT_1000**

**PG**

PG

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11-21-2013
07:51 PM

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Posted in reply to PGStats

11-21-2013 07:51 PM

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11-21-2013
07:51 PM

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Posted in reply to PGStats

11-21-2013 07:51 PM

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11-21-2013 08:23 PM

Thanks a lot lvm. I will switch to MSPL. What are the drawbacks; there must be a good reason why REML is the default method.

PG

PG

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Posted in reply to PGStats

11-21-2013 08:37 PM

ML is biased, especially with small sample sizes. In the simplest possible case, the ML estimate of a one-sample variance involves dividing by n instead of n-1. Thus, variance estimates are biased, but the bias becomes quite small at large n. For unbalanced situations and other complexities, the bias can be seen in the fixed-effect parameters (expected values, slopes, etc.). Thus, it is much better to use REML, which is unbiased, or less biased. Hence, REML is the default. (There are other reasons which I won't get into). In GLIMMIX and MIXED, the primary purpose of AIC, etc., is in comparing models with different random effects, because often there is no a priori best choice for the random effects. One cannot compare directly an AIC from REML with an AIC from ML. If you do want to use information criteria to compare models with different fixed effects, then you must use ML estimation.

Others might not agree, but the bias of ML generally will not be large if the degrees of freedom are large.

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11-21-2013 09:02 PM

Thank you again! That's most helpful. So, a decent strategy would be to chose a model using ML and refit it with REML to get better estimates? - PG

PG

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11-22-2013 10:44 AM

Henderson showed that the REML estimates were equivalent to Bayesian estimates, so REML is the bastard link between frequentists and Bayesians.

At least that's what I vaguely remember from a grad course over 30 years ago...

Anyway, choosing a model is always fraught with difficulties/drawbacks, but you are probably better off using ML (or in GLIMMIX, quasi-likelihood) to select amongst known competing models. My opinion is worth the $0.02 of electrons killed to present it.

Steve Denham