The E-MS Algorithm: Model Selection With Incomplete Data

• 成果类型: Article
• 署名作者: Jiang, Jiming; Thuan Nguyen; Rao, J. Sunil
• 署名单位: University of California System; University of California Davis; Oregon Health & Science University; University of Miami
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2014.948545
• 发表日期: 2015
• 页码: 1136-1147
• 关键词: quantitative trait loci; missing-data; variable selection; fence methods; semiparametric regression; information criterion; maximum-likelihood; repeated outcomes; identification; statistics

摘要

We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized information criteria (GIC), and E-MS with the adaptive fence (AF; Jiang et al.). We prove numerical convergence of the E-MS algorithm as well as consistency in model selection of the limiting model of the E-MS convergence, for E-MS with GIC and E-MS with AF. We study the impact on model selection of different missing data mechanisms. Furthermore, we carry out extensive simulation studies on the finite-sample performance of the E-MS with comparisons to other procedures. The methodology is also illustrated on a real data analysis involving QTL mapping for an agricultural study on barley grains. Supplementary materials for this article are available online.