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Minimum distance regression-type estimates with rates under weak dependence. (English) Zbl 0859.62080
Summary: Under weak dependence, a minimum distance estimate s obtained for a smooth function and its derivatives in a regression-type framework. The upper bound of the risk depends on the Kolmogorov entropy of the underlying space and the mixing coefficient. It is shown that the proposed estimates have the same rate of convergence, in the \(L_1\)-norm sense, as in the independent case.

MSC:
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62G07 Density estimation
62F12 Asymptotic properties of parametric estimators
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