تقرير
Finite-Sample Identification of Linear Regression Models with Residual-Permuted Sums
| العنوان: | Finite-Sample Identification of Linear Regression Models with Residual-Permuted Sums |
|---|---|
| المؤلفون: | Szentpéteri, Szabolcs, Csáji, Balázs Csanád |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Systems and Control, Mathematics - Statistics Theory, Statistics - Machine Learning |
| الوصف: | This letter studies a distribution-free, finite-sample data perturbation (DP) method, the Residual-Permuted Sums (RPS), which is an alternative of the Sign-Perturbed Sums (SPS) algorithm, to construct confidence regions. While SPS assumes independent (but potentially time-varying) noise terms which are symmetric about zero, RPS gets rid of the symmetricity assumption, but assumes i.i.d. noises. The main idea is that RPS permutes the residuals instead of perturbing their signs. This letter introduces RPS in a flexible way, which allows various design-choices. RPS has exact finite sample coverage probabilities and we provide the first proof that these permutation-based confidence regions are uniformly strongly consistent under general assumptions. This means that the RPS regions almost surely shrink around the true parameters as the sample size increases. The ellipsoidal outer-approximation (EOA) of SPS is also extended to RPS, and the effectiveness of RPS is validated by numerical experiments, as well. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.05440 |
| رقم الأكسشن: | edsarx.2406.05440 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |