Spencer Graves <[EMAIL PROTECTED]> writes: > Hi, Peter: What do you do in such situations? Sundar Dorai-Raj > and I have extended "glm" concepts to models driven by a sum of k > independent Poissons, with the a linear model for log(defectRate[i]) > for each source (i = 1:k). To handle convergence problems, etc., I > think we need to use informative Bayes, but we're not there yet. In > any context where things are done more than once [which covers most > human activities], informative Bayes seems sensible. A related > question comes with data representing the differences between Poisson > counts, e.g., with d[i] = X[i]-X[i-1] = the number of new defects > added between steps i-1 and i in a manufacturing process. Most of the > time, d[i] is nonnegative. However, in some cases, it can be > negative, either because of metrology errors in X[i] or because of > defect removal between steps i-1 and i. Comments?
I haven't got all that much experience with it, but obviously, the various algorithms for constrained optimization (box- or otherwise) at least allow you to find a proper maximum likelihood estimator. -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
