论文标题
$ψ$ - 依赖的过程的统计学习
Statistical learning for $ψ$-weakly dependent processes
论文作者
论文摘要
我们考虑了$ψ$ - 依赖依赖的过程的统计学习问题，这些过程统一了大量弱依赖条件，例如混合，关联，$ \ cdots $，建立了经验风险最小化算法的一致性。我们得出概括界限并提供学习率，在某些假设的某些h {Ö}类别上，它接近通常在{\ it I.I.I.D.}情况下获得的常见$ O（n^{ - 1/2}）$。在时间序列预测中的应用以外源协变量的因果模型进行了示例。
We consider statistical learning question for $ψ$-weakly dependent processes, that unifies a large class of weak dependence conditions such as mixing, association,$\cdots$ The consistency of the empirical risk minimization algorithm is established. We derive the generalization bounds and provide the learning rate, which, on some H{ö}lder class of hypothesis, is close to the usual $O(n^{-1/2})$ obtained in the {\it i.i.d.} case. Application to time series prediction is carried out with an example of causal models with exogenous covariates.
