Annals of Applied Statistics

Feature screening for time-varying coefficient models with ultrahigh-dimensional longitudinal data

Wanghuan Chu, Runze Li, and Matthew Reimherr

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Abstract

Motivated by an empirical analysis of the Childhood Asthma Management Project, CAMP, we introduce a new screening procedure for varying coefficient models with ultrahigh-dimensional longitudinal predictor variables. The performance of the proposed procedure is investigated via Monte Carlo simulation. Numerical comparisons indicate that it outperforms existing ones substantially, resulting in significant improvements in explained variability and prediction error. Applying these methods to CAMP, we are able to find a number of potentially important genetic mutations related to lung function, several of which exhibit interesting nonlinear patterns around puberty.

Article information

Source
Ann. Appl. Stat., Volume 10, Number 2 (2016), 596-617.

Dates
Received: January 2016
First available in Project Euclid: 22 July 2016

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1469199886

Digital Object Identifier
doi:10.1214/16-AOAS912

Mathematical Reviews number (MathSciNet)
MR3528353

Zentralblatt MATH identifier
06625662

Keywords
Feature selection time-varying coefficient models ultrahigh-dimensional longitudinal data genome-wide association study functional linear model

Citation

Chu, Wanghuan; Li, Runze; Reimherr, Matthew. Feature screening for time-varying coefficient models with ultrahigh-dimensional longitudinal data. Ann. Appl. Stat. 10 (2016), no. 2, 596--617. doi:10.1214/16-AOAS912. https://projecteuclid.org/euclid.aoas/1469199886


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References

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Supplemental materials

  • Supplement to “Feature screening for time-varying coefficient models with ultrahigh-dimensional longitudinal data”. Theoretical property with technical proofs and additional simulation results for $p=5000$ and $10{,}000$ are given in the online supplement.
    Digital Object Identifier: doi:10.1214/16-AOAS912SUPP
    Supplemental PDF (328 KB)

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