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Model-free feature screening for ultrahigh dimensional censored regression

机译：用于超高维删失回归的无模型特征筛选

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摘要

In this paper we design a sure independent ranking and screening procedure for censored regression (cSIRS, for short) with ultrahigh dimensional covariates. The inverse probability weighted cSIRS procedure is model-free in the sense that it does not specify a parametric or semiparametric regression function between the response variable and the covariates. Thus, it is robust to model mis-specification. This model-free property is very appealing in ultrahigh dimensional data analysis, particularly when there is lack of information for the underlying regression structure. The cSIRS procedure is also robust in the presence of outliers or extreme values as it merely uses the rank of the censored response variable. We establish both the sure screening and the ranking consistency properties for the cSIRS procedure when the number of covariates p satisfies , where a is a positive constant and n is the available sample size. The advantages of cSIRS over existing competitors are demonstrated through comprehensive simulations and an application to the diffuse large-B-cell lymphoma data set.
机译：在本文中，我们设计了具有超高维协变量的受审查的回归（简称cSIRS）的可靠独立排名和筛选程序。逆概率加权cSIRS过程在不指定响应变量和协变量之间的参数或半参数回归函数的意义上是无模型的。因此，对错误指定建模非常可靠。这种无模型的特性在超高维数据分析中非常有吸引力，尤其是在缺乏有关基础回归结构的信息时。 cSIRS过程在存在异常值或极值的情况下也很健壮，因为它仅使用审查响应变量的等级。当协变量数p满足时，我们为cSIRS过程建立确定的筛选和排名一致性属性，其中a为正常数，n为可用样本量。通过全面的模拟以及将其应用于弥漫性大B细胞淋巴瘤数据集，证明了cSIRS与现有竞争对手相比的优势。

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