1 research outputs found
Iterated Feature Screening based on Distance Correlation for Ultrahigh-Dimensional Censored Data with Covariates Measurement Error
Feature screening is an important method to reduce the dimension and capture
informative variables in ultrahigh-dimensional data analysis. Many methods have
been developed for feature screening. These methods, however, are challenged by
complex features pertinent to the data collection as well as the nature of the
data themselves. Typically, incomplete response caused by right-censoring and
covariates measurement error are often accompanying with survival analysis.
Even though there are many methods have been proposed for censored data, little
work has been available when both incomplete response and measurement error
occur simultaneously. In addition, the conventional feature screening methods
may fail to detect the truly important covariates which are marginally
independent of the response variable due to correlations among covariates. In
this paper, we explore this important problem and propose the valid feature
screening method in the presence of survival data with measurement error. In
addition, we also develop the iteration method to improve the accuracy of
selecting all important covariates. Numerical studies are reported to assess
the performance of the proposed method. Finally, we implement the proposed
method to two different real datasets.Comment: 26 pages, 5 table