  In regression, conformal prediction is a general methodology to construct
prediction intervals in a distribution-free manner. Although conformal
prediction guarantees strong statistical property for predictive inference, its
inherent computational challenge has attracted the attention of researchers in
the community. In this paper, we propose a new framework, called Trimmed
Conformal Prediction (TCP), based on two stage procedure, a trimming step and a
prediction step. The idea is to use a preliminary trimming step to
substantially reduce the range of possible values for the prediction interval,
and then applying conformal prediction becomes far more efficient. As is the
case of conformal prediction, TCP can be applied to any regression method, and
further offers both statistical accuracy and computational gains. For a
specific example, we also show how TCP can be implemented in the sparse
regression setting. The experiments on both synthetic and real data validate
the empirical performance of TCP.
