Speaker: Baozhen Wang (Binghamton University)

Title: Predictive Inference with the Jackknife+

Abstract: This talk introduces the jackknife+, which is a novel method (by R. Barber et al.) for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by the quantiles of leave-one-out residuals, the jackknife+ also uses the leave-one-out predictions at the test point to account for the variability in the fitted regression function. Assuming exchangeable training samples, they prove that this crucial modification permits rigorous coverage guarantees regardless of the distribution of the data points, for any algorithm that treats the training points symmetrically.