Minimum distance regression-type estimates with rates under weak dependence
Journal
Annals of the Institute of Statistical Mathematics
Date Issued
1996
DOI
10.1007/BF00054790
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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