Nonparametric Goodness-of-fit Testing under Covariate Shift
This paper develops procedures for nonparametric goodness-of-fit testing under covariate shift, where labelled data are drawn from a source population but goodness-of-fit is evaluated for a target population. The distribution mismatch is quantified by either a bounded moment condition or a sub-exponential tail condition on the target-to-source density ratio. Our method combines truncated importance-weighting kernel ridge regression with a multiplier bootstrap to construct confidence sets for the regression function. The truncation stabilizes the importance- weighting kernel ridge regression as well as the bootstrap calibration, making our approach applicable even when the density ratio ha...
| Authors | Zhen Hou, Dong Xia |
|---|---|
| Primary category | stat.ME |
| Published | 5 August 2026 |
| Last updated at source | 6 August 2026 |
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