I’ve scoured google for an answer to this, but haven’t found anything other than a possibly outdated reference saying specifying a heteroscedastic variance structure isn’t possible in brms.
I don’t have a specific problem I’m working on and this is more of a question of interest, but if it helps in providing a clear answer example can be given for the iris data set.
I should have been a little more clear, my apologies. I meant heteroscedasticity in the case of the variance in variable Y increasing as the value of a continuous predictor variable X increases.
I know the asymmetric laplace family to model different quantiles would be a good choice for this case, but was wondering if there’s an equivalent to the sandwich estimator. I did just find this this paper on the topic, so I hopefully can figure out how to instantiate this in brms, or learn how to write a little Stan code and do it directly in there. I just like toying around with different scenarios. I’m continually impressed with the flexibility of the Bayesian approach.
You could try something like
formula = bf(Y ~ X, sigma ~ X)
Please also see example 2 in https://arxiv.org/pdf/1705.11123.pdf