Fence Methods for Backcross Experiments.
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 (QTL) mapping. However, such modifications often depend upon several factors, such as sample size, prior distribution, and the type of experiment, e.g., backcross, intercross. These changes make it difficult to generalize the methods to all cases. Thefence methodavoids such limitations with a unified approach, and hence can be used more broadly. In this paper, 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.
Department of Public Health and Preventive Medicine, Oregon Health and Science University, Portland, OR 97239, U.S.A.
Thuan
T
Nguyen
Department of Statistics, University of California, Davis, CA 95616, U.S.A.
Jie
J
Peng
Department of Statistics, University of California, Davis, CA 95616, U.S.A.
Jiming
J
Jiang
high-dimensional variable seleciton; model selection; restricted fence (RF)