Parameter tuning in pointwise adaptation using a propagation approach

TL;DR

This paper introduces a new method for tuning parameters in adaptive estimation procedures, improving estimation quality by providing a data-driven approach that balances bias and variance effectively.

Contribution

The paper proposes a novel parameter selection approach for adaptive estimation, ensuring prescribed behavior and near-optimal risk bounds in simple parametric cases.

Findings

The new method achieves near-oracle risk performance.

Numerical results show improved estimation accuracy.

The approach reduces oversmoothing compared to traditional methods.

Abstract

This paper discusses the problem of adaptive estimation of a univariate object like the value of a regression function at a given point or a linear functional in a linear inverse problem. We consider an adaptive procedure originated from Lepski [Theory Probab. Appl. 35 (1990) 454--466.] that selects in a data-driven way one estimate out of a given class of estimates ordered by their variability. A serious problem with using this and similar procedures is the choice of some tuning parameters like thresholds. Numerical results show that the theoretically recommended proposals appear to be too conservative and lead to a strong oversmoothing effect. A careful choice of the parameters of the procedure is extremely important for getting the reasonable quality of estimation. The main contribution of this paper is the new approach for choosing the parameters of the procedure by providing the prescribed behavior of the resulting estimate in the simple parametric situation. We establish a non-asymptotical "oracle" bound, which shows that the estimation risk is, up to a logarithmic multiplier, equal to the risk of the "oracle" estimate that is optimally selected from the given family. A numerical study demonstrates a good performance of the resulting procedure in a number of simulated examples.