Abstract
Under weak dependence, a minimum distance estimate is 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.
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This work was partially supported by a research grant from the Natural Sciences and Engineering Research Council of Canada.
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Roussas, G.G., Yatracos, Y.G. Minimum distance regression-type estimates with rates under weak dependence. Ann Inst Stat Math 48, 267–281 (1996). https://doi.org/10.1007/BF00054790
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DOI: https://doi.org/10.1007/BF00054790