Thank you
Subject: [NMusers] please unsubscibe Date: Tue, 31 May 2011 16:19:45 -0400 From: [email protected] To: 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 De inhoud van dit bericht is vertrouwelijk en alleen bestemd voor de geadresseerde(n). Anderen dan de geadresseerde(n) mogen geen gebruik maken van dit bericht, het niet openbaar maken of op enige wijze verspreiden of vermenigvuldigen. Het UMCG kan niet aansprakelijk gesteld worden voor een incomplete aankomst of vertraging van dit verzonden bericht. The contents of this message are confidential and only intended for the eyes of the addressee(s). Others than the addressee(s) are not allowed to use this message, to make it public or to distribute or multiply this message in any way. The UMCG cannot be held responsible for incomplete reception or delay of this transferred message.
