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- W857905579 abstract "d Signand/or rank-based methods allow to get rid of the classical Gaussian assumptions. The semiparametric testing procedures they generate are indeed valid (thanks to distribution-freeness) under a broad class of distributions. Despite their good efficiency properties, rank-based methods long have been restricted to models involving univariate independent observations. Although the problem of extending these methods to the multivariate setup—which we consider in this paper— arose more than 40 years ago, the first satisfactory multivariate extensions of signs and ranks only came out in the early 90s (while the extension to time series models was made in the 80s). In this paper, we explain the fundamental role played by signs and ranks in univariate problems with good invariance properties, briefly review the multivariate extensions of those concepts, and then present our optimal (in the Le Cam sense) multivariate signed-rank procedures. To the best of our knowledge, these procedures are the only ones preserving (and sometimes even improving) in the multivariate setup, the efficiency properties of optimal univariate signed-rank procedures, as well as their invariance properties—which are the deep fundamental reason for resorting to signs and ranks. ∗Recherche encouragee par un contrat P.A.I. du Gouvernement federal belge, et un contrat A.R.C. de la Communaute francaise de Belgique." @default.
- W857905579 created "2016-06-24" @default.
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- W857905579 date "2003-01-01" @default.
- W857905579 modified "2023-09-26" @default.
- W857905579 title "Procédures optimales fondées sur les rangs multivariés" @default.
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