A statistical perspective of sampling scores for linear regression
This work addresses a statistical challenge in linear regression for researchers, but it is incremental as it builds on existing leverage-based sampling methods by providing new theoretical guarantees and optimal strategies.
The paper tackles the problem of learning a linear model from noisy samples by proposing an estimator to evaluate sampling strategies, deriving its exact mean square error, and introducing optimal sampling scores based on minimizing this error, with numerical simulations confirming the theoretical results.
In this paper, we consider a statistical problem of learning a linear model from noisy samples. Existing work has focused on approximating the least squares solution by using leverage-based scores as an importance sampling distribution. However, no finite sample statistical guarantees and no computationally efficient optimal sampling strategies have been proposed. To evaluate the statistical properties of different sampling strategies, we propose a simple yet effective estimator, which is easy for theoretical analysis and is useful in multitask linear regression. We derive the exact mean square error of the proposed estimator for any given sampling scores. Based on minimizing the mean square error, we propose the optimal sampling scores for both estimator and predictor, and show that they are influenced by the noise-to-signal ratio. Numerical simulations match the theoretical analysis well.