Ravi Varadhan wrote: > It is evident that you do not have enough information in the data to > estimate 9 mixture components. This is clearly indicated by a positive > semi-definite information matrix, S, that is less than full rank. You can > monitor the rank of the information matrix, as you increase the number of > components, and stop when you suspect rank-deficiency. > > Ravi. >
What you say is likely to be true, but I was interested to see if this was reflected in some of the traditional model selection criteria (AIC, BIC, ...). In this case numerical problems caused by overfitting prevent the calculation of a diagnostic measure for overfitting. Incidentally here other measures of overfitting that I was able to calculate continue to indicate underfitting. Of course in the mixture model case these measures are heuristic only as the assumptions behind their asymptotic justification are not valid. Murray -- Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html Department of Statistics, University of Waikato, Hamilton, New Zealand Email: [EMAIL PROTECTED] Fax 7 838 4155 Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 1395 862 ______________________________________________ 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.
