Volume 24, 2020
|Page(s)||435 - 453|
|Published online||06 October 2020|
Raking-ratio empirical process with auxiliary information learning
Institut de Mathématiques de Toulouse, Université Paul Sabatier UMR5219,
* Corresponding author: firstname.lastname@example.org
Accepted: 28 April 2020
The raking-ratio method is a statistical and computational method which adjusts the empirical measure to match the true probability of sets of a finite partition. The asymptotic behavior of the raking-ratio empirical process indexed by a class of functions is studied when the auxiliary information is given by estimates. These estimates are supposed to result from the learning of the probability of sets of partitions from another sample larger than the sample of the statistician, as in the case of two-stage sampling surveys. Under some metric entropy hypothesis and conditions on the size of the information source sample, the strong approximation of this process and in particular the weak convergence are established. Under these conditions, the asymptotic behavior of the new process is the same as the classical raking-ratio empirical process. Some possible statistical applications of these results are also given, like the strengthening of the Z-test and the chi-square goodness of fit test.
Mathematics Subject Classification: 62G09 / 62G20 / 60F17 / 60F05
Key words: Uniform central limit theorems / nonparametric statistics / empirical processes / raking ratio process / auxiliary information / learning
© The authors. Published by EDP Sciences, SMAI 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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