Speaker
Description
Abstract
In the era of big data, several sampling approaches are proposed to reduce costs (and time) and to help in informed decision making. Most of these proposals require the specification of a model for the big data. This model assumption, as well as the possible presence of outliers in the big dataset, represent a limitation for the most commonly applied subsampling criterions.
The task of avoiding outliers in a subsample of data was addressed by Deldossi et al. (2023), who introduced non-informative and informative exchange algorithms to select “nearly” D-optimal subsets without outliers in a linear regression model. In this study, we extend their proposal to account for model uncertainty. More precisely, we propose a model robust approach where a set of candidate models is considered; the optimal subset is obtained by merging the subsamples that would be selected by applying the approach of Deldossi et al. (2023) if each model was considered as the true generating process.
The approach is applied in a simulation study and some comparisons with other subsampling procedures are provided.
References
Deldossi, L., Pesce, E., Tommasi, C. (2023) Accounting for outliers in optimal subsampling methods, Statistical Papers, https://doi.org/10.1007/s00362-023-01422-3.
| Classification | Both methodology and application |
|---|---|
| Keywords | Active learning, D-optimality, Subsampling |