Fence methods for backcross experiments

Thuan Nguyen, Jie Peng, Jiming Jiang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Model search strategies play an important role in finding simultaneous susceptibility genes that are associated with a trait. More particularly, model selection via the information criteria, such as the BIC with modifications, have received considerable attention in quantitative trait loci mapping. However, such modifications often depend upon several factors, such as sample size, prior distribution, and the type of experiment, for example, backcross, intercross. These changes make it difficult to generalize the methods to all cases. The fence method avoids such limitations with a unified approach, and hence can be used more broadly. In this article, this method is studied in the case of backcross experiments throughout a series of simulation studies. The results are compared with those of the modified BIC method as well as some of the most popular shrinkage methods for model selection.

Original languageEnglish (US)
Pages (from-to)644-662
Number of pages19
JournalJournal of Statistical Computation and Simulation
Issue number3
StatePublished - Mar 2014


  • high-dimensional variable selection
  • model selection
  • restricted fence

ASJC Scopus subject areas

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


Dive into the research topics of 'Fence methods for backcross experiments'. Together they form a unique fingerprint.

Cite this