Restricted fence method for covariate selection in longitudinal data analysis

Thuan Nguyen, Jiming Jiang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Fence method (Jiang and others 2008. Fence methods for mixed model selection. Annals of Statistics 36, 1669-1692) is a recently proposed strategy for model selection. It was motivated by the limitation of the traditional information criteria in selecting parsimonious models in some nonconventional situations, such as mixed model selection. Jiang and others (2009. A simplified adaptive fence procedure, Statistics & Probability Letters 79, 625-629) simplified the adaptive fence method of Jiang and others (2008) to make it more suitable and convenient to use in a wide variety of problems. Still, the current modification encounters computational difficulties when applied to high-dimensional and complex problems. To address this concern, we proposed a restricted fence procedure that combines the idea of the fence with that of the restricted maximum likelihood. Furthermore, we propose to use the wild bootstrap for choosing adaptively the tuning parameter used in the restricted fence. We focus on problems of longitudinal studies and demonstrate the performance of the new procedure and its comparison with other procedures of variable selection, including the information criteria and shrinkage methods, in simulation studies. The method is further illustrated by an example of real-data analysis.

Original languageEnglish (US)
Pages (from-to)303-314
Number of pages12
Issue number2
StatePublished - Apr 1 2012


  • Covariate variable selection
  • Longitudinal data
  • Restricted fence method
  • Wild boostrapping

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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