On 10/14/2010 06:42 PM, Viechtbauer Wolfgang (STAT) wrote: > Since the number of parameters then rises linearly with the number of > subjects, this may be a case where maximum likelihood theory breaks > down, that is, a Neyman-Scott problem.
My thought too. The basic structure is close to the Rasch (IRT) model, for which it is known that you need conditional inference to get consistent estimates of item parameters. My gut feeling is that it could depend on the size of the lambda, i.e. whether we are looking at many sparse subject-specific tables. The whole thing is also quite similar to classical Epi techniques like Mantel-Haenszel, conditional logit, etc., without being spot-on since those methods deal with the interaction term of stratified 2x2 tables. Then again, isn't the structure here that conditionally on the sum of the Y_ijk over j and k, we have just a bunch of multinomially distributed tables, with independence and the SAME row and col parameters? So just sum them all and estimate.... I.e., the conditional analysis is trivial, and if it doesn't coincide with full ML, the latter is most likely wrong anyway! (With reservation for this being Saturday morning, etc.) -- Peter Dalgaard Center for Statistics, Copenhagen Business School Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
