 Bayes quantile under asymmetric absolute-error loss

For a posterior distribution with finite first moment, the posterior risk derivative at continuity points is $(\gamma +\delta )F(a)-\gamma$. Convexity therefore makes a posterior $\gamma /(\gamma +\delta )$-quantile a Bayes estimator. With atoms, the condition is $F(a^{-})\le \gamma /(\gamma +\delta )\le F(a)$. Increasing the cost of underestimation moves the optimal estimate upward.