Estimation of a semiparametric transformation model

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dc.creatorLinton, Oliver
dc.creatorSperlich, Stefan
dc.creatorVan Keilegom, Ingrid
dc.date2008-04-04
dc.date.accessioned2026-07-07T12:18:07Z
dc.date.available2026-07-07T12:18:07Z
dc.descriptionThis paper proposes consistent estimators for transformation parameters in semiparametric models. The problem is to find the optimal transformation into the space of models with a predetermined regression structure like additive or multiplicative separability. We give results for the estimation of the transformation when the rest of the model is estimated non- or semi-parametrically and fulfills some consistency conditions. We propose two methods for the estimation of the transformation parameter: maximizing a profile likelihood function or minimizing the mean squared distance from independence. First the problem of identification of such models is discussed. We then state asymptotic results for a general class of nonparametric estimators. Finally, we give some particular examples of nonparametric estimators of transformed separable models. The small sample performance is studied in several simulations.
dc.descriptionPublished in at http://dx.doi.org/10.1214/009053607000000848 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0804.0719
dc.identifierhttp://arxiv.org/abs/0804.0719
dc.identifierAnnals of Statistics 2008, Vol. 36, No. 2, 686-718
dc.identifierdoi:10.1214/009053607000000848
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/212300
dc.subjectStatistics Theory
dc.subject62E20, 62F12, 62G05, 62G08, 62G20 (Primary)
dc.titleEstimation of a semiparametric transformation model
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