Improved conformalized quantile regression. (arXiv:2207.02808v7 [stat.ML] UPDATED)

Conformalized quantile regression is a procedure that inherits the advantages
of conformal prediction and quantile regression. That is, we use quantile
regression to estimate the true conditional quantile and then apply a conformal
step on a calibration set to ensure marginal coverage. In this way, we get
adaptive prediction intervals that account for heteroscedasticity. However, the
aforementioned conformal step lacks adaptiveness as described in (Romano et
al., 2019). To overcome this limitation, instead of applying a single conformal
step after …

arxiv quantile regression