seminars:stat:201022
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| + | The authors extend conformal prediction methodology beyond the | ||
| + | case of exchangeable data. In particular, they show that a weighted | ||
| + | version of conformal prediction can be used to compute distribution-free | ||
| + | prediction intervals for problems in which the test and training covariate | ||
| + | distributions differ, but the likelihood ratio between these two | ||
| + | distributions is known—or, in practice, can be estimated accurately with | ||
| + | access to a large set of unlabeled data (test covariate points). Their | ||
| + | weighted extension of conformal prediction also applies more generally, to | ||
| + | settings in which the data satis&# | ||
| + | exchangeability. | ||
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