Sparse-penalized deep neural networks estimator under weak dependence
We consider the nonparametric regression and the classification problems for $\psi$-weakly dependent processes. This weak dependence structure is more general than conditions such as, mixing, association, $\ldots$. A penalized estimation method for sparse deep neural networks is performed. In both nonparametric regression and binary classification problems, we establish oracle inequalities for the excess risk of the sparse-penalized deep neural networks estimators. Convergence rates of the excess risk of these estimators are also derived. The simulation results displayed show that, the proposed estimators overall work well than the non penalized estimators.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationClassificationregressionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Penalized deep neural networks estimator with general loss functions under weak dependence
This paper carries out sparse-penalized deep neural networks predictors for learning weakly dependent processes, with a broad class of loss functions. We deal with a general framework that includes, regression estimation…
High-Dimensional Covariance Decomposition into Sparse Markov and Independence Models
Fitting high-dimensional data involves a delicate tradeoff between faithful representation and the use of sparse models. Too often, sparsity assumptions on the fitted model are too restrictive to provide a faithful repre…
Vocal Bursts Intensity PredictionOn the instrumental variable estimation with many weak and invalid instruments
We discuss the fundamental issue of identification in linear instrumental variable (IV) models with unknown IV validity. With the assumption of the "sparsest rule", which is equivalent to the plurality rule but becomes o…
Learning Local Dependence In Ordered Data
In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions …
regressionExpected Shortfall LASSO
We propose an $\ell_1$-penalized estimator for high-dimensional models of Expected Shortfall (ES). The estimator is obtained as the solution to a least-squares problem for an auxiliary dependent variable, which is define…
Time Series