Adaptive deep nonparametric regression from dependent data under covariate shift
Covariate shift often occurs because, in many real applications, the source and the target observations may be generated from different distributions. In this case, the standard metric under the source distribution is not appropriate. This paper considers deep neural network estimators for nonparametric quantile and Huber regression under covariate shift and from dependent observations. We deal with a generalized Bernstein-type inequality that is satisfied by many classical models, including i.i.d. observations, $φ$-mixing, strong mixing, and $\mathcal{C}$-mixing processes. To perform the covariate shift phenomenon, we propose a sparse-penalized deep neural network (SPDNN) estimator that ...
| Authors | William Kengne, Ehud Mossa Ockegna |
|---|---|
| Primary category | stat.ML |
| Published | 22 July 2026 |
| Last updated at source | 22 July 2026 |
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