TY - JOUR
T1 - Fence methods for backcross experiments
AU - Nguyen, Thuan
AU - Peng, Jie
AU - Jiang, Jiming
N1 - Funding Information:
This study was made possible with support from the Oregon Clinical and Translation Research Institute (OCTRI), grant # UL1 RR024140 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. The authors are grateful to a referee for his/her thoughtful comments that led to the improvement of the manuscript.
PY - 2014/3
Y1 - 2014/3
N2 - 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.
AB - 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.
KW - high-dimensional variable selection
KW - model selection
KW - restricted fence
UR - http://www.scopus.com/inward/record.url?scp=84889257742&partnerID=8YFLogxK
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U2 - 10.1080/00949655.2012.721885
DO - 10.1080/00949655.2012.721885
M3 - Article
AN - SCOPUS:84889257742
SN - 0094-9655
VL - 84
SP - 644
EP - 662
JO - Journal of Statistical Computation and Simulation
JF - Journal of Statistical Computation and Simulation
IS - 3
ER -