1. See P.S. above.

2. I guess we should’ve followed basic principles of econ and charged a nominal $10 admissions fee so as to restrict to people who’d be likely to show up.

]]>All methods require making untenable assumptions. So-called nonparametric methods work by pooling in some way, thus assuming constancy or additivity or some similar assump. The method you describe could well be useful, but it makes assumps, no doubt about that.

]]>It looks like v1.4.0 of brms introduced quantile regression “Fit quantile regression models via family asym_laplace (asymmetric Laplace distribution).” But that was done by Paul implementing an asymmetric Laplace distribution on his end and not in the underlying RSTAN code?

]]>Sure, but with a Bayesian model you’re simultaneously modeling all the quantiles. I guess I can see there being a niche for certain specialized methods applied just to quantiles, but I certainly don’t see this topic as so central that we “ought to have something” on it!

]]>https://groups.google.com/forum/#!topic/stan-users/iw5u9wYK2hI

]]>From wikipedia, I see that “quantile regression aims at estimating either the conditional median or other quantiles of the response variable.” If you fit a Bayesian model, you can compute these quantiles by simulating predictive values. That’s what I was talking about in my comment above.

]]>That’s a good idea. We’ll get some abstracts and post them!

]]>In Bayesian inference you’re estimating the entire distribution, so no need for special techniques for quantile regression. You can just fit the Bayesian model and then get inferences for quantiles if that’s what you want.

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