Weak dependence beyond mixing and asymptotics for nonparametric regression

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Some Results on the Control of the False Discovery Rate under Dependence (2007, background): “Doukhan & Louhichi (1999) and then Nze et al. (2002) define a more general framework for weak dependence, which includes processes that satisfy mixing and association conditions, together with cases in which these two properties fail to hold, like Bernoulli shifts driven by discrete innovations.”