Inference for censored quantile regression models in longitudinal studies

dc.creatorWang, Huixia Judy
dc.creatorFygenson, Mendel
dc.date2009-04-01
dc.date.accessioned2026-07-07T12:59:00Z
dc.date.available2026-07-07T12:59:00Z
dc.descriptionWe develop inference procedures for longitudinal data where some of the measurements are censored by fixed constants. We consider a semi-parametric quantile regression model that makes no distributional assumptions. Our research is motivated by the lack of proper inference procedures for data from biomedical studies where measurements are censored due to a fixed quantification limit. In such studies the focus is often on testing hypotheses about treatment equality. To this end, we propose a rank score test for large sample inference on a subset of the covariates. We demonstrate the importance of accounting for both censoring and intra-subject dependency and evaluate the performance of our proposed methodology in a simulation study. We then apply the proposed inference procedures to data from an AIDS-related clinical trial. We conclude that our framework and proposed methodology is very valuable for differentiating the influences of predictors at different locations in the conditional distribution of a response variable.
dc.descriptionPublished in at http://dx.doi.org/10.1214/07-AOS564 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0904.0080
dc.identifierhttp://arxiv.org/abs/0904.0080
dc.identifierAnnals of Statistics 2009, Vol. 37, No. 2, 756-781
dc.identifierdoi:10.1214/07-AOS564
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/225436
dc.subjectStatistics Theory
dc.subject62G99 (Primary) 62N01, 62P10 (Secondary)
dc.titleInference for censored quantile regression models in longitudinal studies
dc.typetext

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