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Buckley-James Type Estimator for Censored Data with Covariates Missing by Design

机译:设计缺失协变量的删失数据的Buckley-James类型估计器

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The Buckley-James estimator (BJE) is a well-known estimator for linear regression models with censored data. Ritov has generalized the BJE to a semiparametric setting and demonstrated that his class of Buckley-James type estimators is asymptotically equivalent to the class of rank-based estimators proposed by Tsiatis. In this article, we revisit such relationship in censored data with covariates missing by design. By exploring a similar relationship between our proposed class of Buckley-James type estimating functions to the class of rank-based estimating functions recently generalized by Nan, Kalbfleisch and Yu, we establish asymptotic properties of our proposed estimators. We also conduct numerical studies to compare asymptotic efficiencies from various estimators.
机译:Buckley-James估计器(BJE)是带有删失数据的线性回归模型的著名估计器。 Ritov已将BJE推广到一个半参数设置,并证明了他的Buckley-James类型估计器的类别渐近等效于Tsiatis提出的基于等级的估计器的类别。在本文中,我们将重新审查被检查数据中的这种关系,并且设计会丢失协变量。通过探索拟议的Buckley-James类型估计函数类与Nan,Kalbfleisch和Yu最近推广的基于秩的估计函数类之间的相似关系,我们建立了拟议估计器的渐近性质。我们还进行了数值研究,以比较各种估计量的渐近效率。

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