Penalized log-likelihood estimation for partly linear transformation models with current status data

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dc.creatorMa, Shuangge
dc.creatorKosorok, Michael R.
dc.date2006-02-11
dc.date.accessioned2026-07-07T08:07:32Z
dc.date.available2026-07-07T08:07:32Z
dc.descriptionWe consider partly linear transformation models applied to current status data. The unknown quantities are the transformation function, a linear regression parameter and a nonparametric regression effect. It is shown that the penalized MLE for the regression parameter is asymptotically normal and efficient and converges at the parametric rate, although the penalized MLE for the transformation function and nonparametric regression effect are only $n^{1/3}$ consistent. Inference for the regression parameter based on a block jackknife is investigated. We also study computational issues and demonstrate the proposed methodology with a simulation study. The transformation models and partly linear regression terms, coupled with new estimation and inference techniques, provide flexible alternatives to the Cox model for current status data analysis.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053605000000444 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0602243
dc.identifierhttp://arxiv.org/abs/math/0602243
dc.identifierAnnals of Statistics 2005, Vol. 33, No. 5, 2256-2290
dc.identifierdoi:10.1214/009053605000000444
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130965
dc.subjectStatistics Theory
dc.subject62G08, 60F05 (Primary) 62G20, 62B10 (Secondary)
dc.titlePenalized log-likelihood estimation for partly linear transformation models with current status data
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