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From: Robert Patten <[email protected]> To: [email protected] Sent: Tuesday, May 31, 2011 4:19 PM Subject: [NMusers] please unsubscibe Thank you, Rob From:[email protected] [mailto:[email protected]] On Behalf Of Eleveld, DJ Sent: Tuesday, May 31, 2011 2:48 PM To: ???; [email protected] Subject: RE: [NMusers] question about shinkage Hi Li, Well, do you have rich data and a small number of subjects? How much shrinkage exactly? A very small negative number might just be due to (hopefully) non-important numerical issues. It could also be due to early termination of the estimation, not doing enough iterations, problems with rounding errors, etc. The use of shrinkage to diagnose model problems isnt powerful enough to try to solve a problem without knowing anything about the model or the data. So, it depends on your problem. What you usually encounter is that high shrinkage means that a dataset is not informative enough to estimate a paramater in the individuals. I would interpret negative shrinkage as meaning that something went wrong with the estimation. In that case you cant trust the resulting estimations (or shrinkage for that matter) to be meaningful anyway. You might want to look a constructing likelihood profiles for you model estimations as well. I find they work nicely in conjuction with considering shrinkage. Douglas Eleveld -----Original Message----- From: [email protected] on behalf of ??? Sent: Tue 5/31/2011 4:33 PM To: [email protected] Subject: [NMusers] question about shinkage Hi dear all, I have a question about shinkage. I read an article about shinkage (Radojka M. Savic and Mats O. Karlsson Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions 2009) and try to use shinkage to diagnose my model. An ETA-shinkage is negative in my result. According to the article, negative shinkage may occur in the situation where a parameter variance is fixed to a lower value than the true value or in rich data from a small number of subjects. I wonder that if the parameter variance is fixed, shinkage is 100% in my result. And if it is the data problem, why is the shinkage of this kind of data negative? Besides, I wonder that whether the negative shinkage indicate the model misspecification? How important is shinkage to diagnose a model? Is it more used to evaluate the relationship between the covariate and parameters or to choose a model? Thanks! Li Mengyao