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From | Maarten buis <maartenbuis@yahoo.co.uk> |
To | statalist@hsphsun2.harvard.edu |
Subject | Re: st: Kenward-Roger method in Stata? |
Date | Fri, 4 Jun 2010 03:33:35 -0700 (PDT) |
--- On Fri, 4/6/10, Seed, Paul wrote: > SAS's command PROC MIXED has an option " ddfm=KR " > which implements Kenward & Roger (1997), > improving on the asymptotic estimation of > SE, CI significance etc. There is some evidence that > it performs well with missing data and small samples > > Has anyone attempted to implement this in Stata, for > either -xtgee- or -xtmixed-? Not that I know, but I would start with trying to figure out whether you would actually need it. One way of figureing out whether this is true for your dataset is simulation: 1) estimate your model of interest, for the purpose of this simulation this will be the estimates in the "populaiton". 2) use -bsample- to draw a sample from your data 3) estimate your model of interest 4) test whether the parameter of interest is equal to the "population"-parameter, and return the p-value. 5) repeat steps 2-4 many times (use the -simulate- command for that.) If the standard standard errors work wel for your data, then these p-value should follow a uniform distribution. If I am allowed some shameless self-promotion, I would say that I like -hangroot- for that. (see: -ssc d hangroot- and the 7th graph on <http://www.maartenbuis.nl/software/hangroot.html>). You can also look at the coverage: if 5% of the samples have a p-value less than .05. As the information necesary for computing this coverage is contained in only 5% of the samples, you need a great many of those to get a reliable estimate, say 10,000, which means you would expect around 500 rejections. Hope this helps, Maarten -------------------------- Maarten L. Buis Institut fuer Soziologie Universitaet Tuebingen Wilhelmstrasse 36 72074 Tuebingen Germany http://www.maartenbuis.nl -------------------------- * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/