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dc.contributor.authorVeraverbeke, Noël
dc.contributor.authorGijbels, Irène
dc.contributor.authorOmelka, Marek
dc.date.accessioned2016-03-01T06:00:36Z
dc.date.available2016-03-01T06:00:36Z
dc.date.issued2014
dc.identifier.citationVeraverbeke, N. et al. 2014. Preadjusted non-parametric estimation of a conditional distribution function. Journal of the Royal Statistical Society, B: Statistical methodology, 76(2):399-438. [http://dx.doi.org/10.1111/rssb.12041]en_US
dc.identifier.issn1369-7412
dc.identifier.issn1467-9868 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/16483
dc.identifier.urihttp://dx.doi.org/10.1111/rssb.12041
dc.identifier.urihttp://onlinelibrary.wiley.com/doi/10.1111/rssb.12041/epdf
dc.description.abstractThe paper deals with non-parametric estimation of a conditional distribution function. We suggest a method of preadjusting the original observations non-parametrically through location and scale, to reduce the bias of the estimator.We derive the asymptotic properties of the estimator proposed. A simulation study investigating the finite sample performances of the estimators discussed is provided and reveals the gain that can be achieved. It is also shown how the idea of the preadjusting opens the path to improved estimators in other settings such as conditional quantile and density estimation, and conditional survival function estimation in the case of censored dataen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.subjectConditional distributionen_US
dc.subjectempirical processen_US
dc.subjectkernel methodsen_US
dc.subjectlocal linear weightsen_US
dc.subjectsmoothingen_US
dc.titlePreadjusted non-parametric estimation of a conditional distribution functionen_US
dc.typeArticleen_US
dc.contributor.researchID22051880 - Veraverbeke, Noël Daniel


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