Becoming distributions utilizing bayesmh
This put up was written collectively with Yulia Marchenko, Government Director of Statistics, StataCorp.
As of replace 03 Mar 2016, bayesmh supplies a extra handy manner of becoming distributions to the result variable. By design, bayesmh is a regression command, which fashions the imply of the result distribution as a perform of predictors. There are circumstances after we do not need any predictors and need to mannequin the result distribution instantly. For instance, we could need to match a Poisson distribution or a binomial distribution to our final result. This may now be performed by specifying one of many 4 new distributions supported by bayesmh within the chance() choice: dexponential(), dbernoulli(), dbinomial(), or dpoisson(). Beforehand, the suboption noglmtransform of bayesmh‘s choice chance() was used to suit the exponential, binomial, and Poisson distributions to the result variable. This suboption continues to work however is now undocumented.
For examples, see Beta-binomial mannequin, Bayesian evaluation of change-point drawback, and Merchandise response concept beneath Remarks and examples in [BAYES] bayesmh.
We have now additionally up to date our earlier “Bayesian binary merchandise response concept fashions utilizing bayesmh” weblog entry to make use of the brand new dbernoulli() specification when becoming 3PL, 4PL, and 5PL IRT fashions.
