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March 18, 2026Biometrics

Variable selection in functional linear Cox model

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Authors

YYYuanzhen YueSSStella SelfYWY. Wu

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Overview

A novel variable selection method improves survival model accuracy, focusing on high-dimensional physiological signals.

Key Points

  • To develop a variable selection method for a functional linear Cox model that handles high-dimensional physiological data.
  • Proposed a spline-based semiparametric estimation approach for functional coefficients.
  • Implemented a group minimax concave type penalty for integrating smoothness and sparsity.
  • Used an efficient group descent algorithm for optimization of the model.
  • Automated procedure for selecting optimal smoothing and sparsity parameters.
  • Demonstrated accurate variable selection and estimation through simulation studies.
  • Identified key patterns in physical activity and demographic predictors linked to all-cause mortality in a cohort from 2003-2006.
  • Showed significant associations between daily physical activity distributions and mortality among older adults.

Cite This Study

Yue et al. (2026) studied this question.

synapsesocial.com/papers/69ba42fb4e9516ffd37a3c8ahttps://doi.org/10.1093/biomtc/ujag044
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