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- W1580423062 abstract "<!-- *** Custom HTML *** --> Rank-based inference and, in particular, R-estimation, is a red thread running through Jana Jurečková’s entire scientific career, starting with her dissertation in 1967, where she laid the foundations of an extension to linear regression of the R-estimation methods that had recently been proposed by Hodges and Lehmann [13]. Cross-information quantities in that context play an essential role. In location/regression problems, these quantities take the form <i>∫</i><sub>0</sub><sup>1</sup><i>φ</i>(<i>u</i>)<i>φ</i><sub><i>g</i></sub>(<i>u</i>) d<i>u</i> where <i>φ</i> is a score function and <i>φ</i><sub><i>g</i></sub>(<i>u</i>):=<i>g</i><i>'</i>(<i>G</i><sup><i>−</i>1</sup>(<i>u</i>))/<i>g</i>(<i>G</i><sup><i>−</i>1</sup>(<i>u</i>)) is the log-derivative of the unknown actual underlying density <i>g</i> computed at the quantile <i>G</i><sup><i>−</i>1</sup>(<i>u</i>); in other models, they involve more general scores. Such quantities appear in the local powers of rank tests and the asymptotic variance of R-estimators. Estimating them consistently is a delicate problem that has been extensively considered in the literature. We provide here a new, flexible, and very general method for that problem, which furthermore applies well beyond the traditional case of regression models." @default.
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- W1580423062 date "2010-01-01" @default.
- W1580423062 modified "2023-09-25" @default.
- W1580423062 title "On the estimation of cross-information quantities in rank-based inference" @default.
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- W1580423062 doi "https://doi.org/10.1214/10-imscoll704" @default.
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