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  • A fast adaptive Lasso for the cox regression via safe screening rules

    Author(s)
    Zhang, Z
    Shen, Z
    Wang, H
    Ng, SK
    Griffith University Author(s)
    Ng, Shu Kay Angus
    Year published
    2021
    Metadata
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    Abstract
    Some interesting recent studies have shown that safe feature elimination screening algorithms are useful alternatives in solving large scale and/or ultra-high-dimensional Lasso-type problems. However, to the best of our knowledge, the plausibility of adapting the safe feature elimination screening algorithm to survival models is rarely explored. In this study, we first derive the safe feature elimination screening rule for adaptive Lasso Cox model. Then, using both simulated and real-world datasets, we demonstrate that the resulting algorithm can outperform Lasso Cox and adaptive Lasso Cox prediction methods in terms of its ...
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    Some interesting recent studies have shown that safe feature elimination screening algorithms are useful alternatives in solving large scale and/or ultra-high-dimensional Lasso-type problems. However, to the best of our knowledge, the plausibility of adapting the safe feature elimination screening algorithm to survival models is rarely explored. In this study, we first derive the safe feature elimination screening rule for adaptive Lasso Cox model. Then, using both simulated and real-world datasets, we demonstrate that the resulting algorithm can outperform Lasso Cox and adaptive Lasso Cox prediction methods in terms of its predictive performance. In addition to its good predictive performance, we illustrate that the proposed algorithm has a key computational advantage over the above competing methods in terms of computation efficiency.
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    Journal Title
    Journal of Statistical Computation and Simulation
    DOI
    https://doi.org/10.1080/00949655.2021.1914043
    Funder(s)
    ARC
    Grant identifier(s)
    DP170100907
    Note
    This publication has been entered in Griffith Research Online as an advanced online version.
    Subject
    Statistics
    Applied economics
    Econometrics
    Publication URI
    http://hdl.handle.net/10072/404260
    Collection
    • Journal articles

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