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Trimmed Conformal Prediction for High-Dimensional Models · Around

2016

Trimmed Conformal Prediction for High-Dimensional Models

Chen, Wenyu, Wang, Zhaokai, Ha, Wooseok et al.

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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.

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