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Erschienen in:
MiRNA–Gene Activity Interaction Networks (miGAIN): Integrated Joint Models of miRNA–Gene Targeting and Disturbance in Signaling Pathways Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

Robust Feature Screening for Ultrahigh-Dimensional Censored Data Subject to Measurement Error

verfasst von : Li-Pang Chen, Grace Y. Yi

Erschienen in: Advances and Innovations in Statistics and Data Science

Verlag: Springer International Publishing

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Abstract

Feature screening is commonly used to handle ultrahigh-dimensional data prior to conducting a formal data analysis. While various feature screening methods have been developed in the literature, research gaps still exist. The existing methods usually make an implicit assumption that data are accurately measured. This requirement, however, is frequently violated in applications. In this chapter, we consider error-prone ultrahigh-dimensional survival data and propose a robust feature screening method. We develop an iteration algorithm to improve the performance of retaining all informative covariates. Theoretical results are established for the proposed method. Simulation studies are reported to assess the performance of the proposed method, together with an application of the proposed method to handle a mantle cell lymphoma microarray dataset.

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Vorheriges Kapitel MiRNA–Gene Activity Interaction Networks (miGAIN): Integrated Joint Models of miRNA–Gene Targeting and Disturbance in Signaling Pathways
Nächstes Kapitel Simultaneous Control of False Discovery Rate and Sensitivity Using Least Angle Regressions in High-Dimensional Data Analysis
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Metadaten
Titel
Robust Feature Screening for Ultrahigh-Dimensional Censored Data Subject to Measurement Error
verfasst von
Li-Pang Chen
Grace Y. Yi
Copyright-Jahr
2022
Buch
Advances and Innovations in Statistics and Data Science

Print ISBN: 978-3-031-08328-0

Electronic ISBN: 978-3-031-08329-7

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-031-08329-7_2

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