The paper proves nonasymptotic coverage guarantees for bootstrap confidence balls around a truncated importance-weighted kernel ridge estimator in the target L2 metric under covariate shift.
Effects of sampling skewness of the importance-weighted risk estimator on model selection
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has desirable characteristics such as unbiasedness, consistency and low computational complexity. However, weighting can have a detrimental effect on an estimator as well. In this work, we empirically show that the sampling distribution of an importance-weighted estimator can be skewed. For sample selection bias settings, and for small sample sizes, the importance-weighted risk estimator produces overestimates for datasets in the body of the sampling distribution, i.e. the majority of cases, and large underestimates for data sets in the tail of the sampling distribution. These over- and underestimates of the risk lead to suboptimal regularization parameters when used for importance-weighted validation.
fields
stat.ME 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Nonparametric Goodness-of-fit Testing under Covariate Shift
The paper proves nonasymptotic coverage guarantees for bootstrap confidence balls around a truncated importance-weighted kernel ridge estimator in the target L2 metric under covariate shift.