Rates in strong laws of large numbers in nonparametric statistics
Résumé
Fazekas and Klesov (2000) found conditions for almost sure convergence rates in the law of large numbers that effectively can be applied if maximal inequalities are available. In the spirit of M´oricz (1976), we aim at using those conditions in a weakly dependent framework, and this trick is proved to be quite efficient, first in the standard law of large numbers and second in the nonparametric estimation context where rates of convergence of the density kernel estimates are also obtained.
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