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#11




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If instead you want your complement to be some specific (higher) value, you could add a dummy indicator variable that aligns with that class, and then fix the dummy variable with an offset. 
#12




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But whenever you're dealing with a class with very little data available and you want to utilize credibilitybased models to estimate the parameters, you're confronted with the decision of what to use as the complement of credibility. As examsare One complement that you could consider is the current relativity for that class. Another consideration might be to determine an approximate indicated relativity if you "spread out" the infrequent large losses over a longer time horizon (say, 5 years).
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#13




This could be handled in a Bayesian framework. If a "normal" market has a mean of X and you believe this other market may be 30% higher, could set the prior to Beta ~ N(1.3X, sigma). Sigma would be set based on how confident you are in the 30% figure (or to put it another way, how much you want the model to "listen" to the data).
Of course a normal distribution may not work either but there are others that can be used instead. 
#15




I need to read up on both the paper from examsarehard & the books from AMedActuary.
Still waiting on the Bayesian books from Amazon. Any thoughts on the highlevel differences b/ GLMM & Bayesian? 
#16




I believe a GLMM can be interpreted as a specific form of the Bayesian GLM where the prior distribution of the parameters is normally distributed with mean 0.
edit: Only the credibilityweighted parameters have normal priors. Any noncredibilityweighted parameters (like in a regular GLM) would have flat priors. Last edited by examsarehard; 04102017 at 03:59 PM.. Reason: clarity 
#17




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I *think* I did that, but the low cred class factors is the same between 1) let the glm solves for the class factor 2) add a dummy indicator variable that aligns with that class, and then fix the dummy variable with an offset Last edited by hellomath; 07132017 at 11:49 PM.. 
#18




In R: the bayesglm() function in the arm package is awesome and much quicker than using STAN/JAGS for a simple bayesian GLM.
https://cran.rproject.org/web/packages/arm/arm.pdf 
#19




I would also recommend 'rstanarm' for basic bayesian models. See the function 'stan_glm.'
https://CRAN.Rproject.org/package=rstanarm 
#20




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