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

Wanghuan Chu, Runze Li, Matthew Reimherr

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

Motivated by an empirical analysis of the Childhood Asthma Management Project, CAMP, we introduce a new screening procedure for varying co-efficient 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.

Original languageEnglish (US)
Pages (from-to)596-617
Number of pages22
JournalAnnals of Applied Statistics
Volume10
Issue number2
DOIs
Publication statusPublished - Jun 2016

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All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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