发布日期：2014-09-12点击： 发布人：统数院
主题：Inference for mixed models of ANOVA type with high-dimensional data
主讲人：陈飞 博士
时间：2014年9月15日（周一）下午 2：30
地点：北院卓远楼305
主办单位：统计与数学学院
摘要：Inference for variance components in linear mixed models of ANOVA type, including estimation and testing, has been investigated when the number of fixed effects is fixed. However, for high-dimensional data, this number is large and would be regarded as a divergent value as the sample size goes to infinity. In this paper, existing tests are extended to handle this problem with a sparse model structure. To avoid the impact from insignificant fixed effects, the proposed tests are post-selection-based with an orthogonality-based selection of SCAD type applied to selecting significant fixed effects into working model. The selection and estimation of fixed effects are under the assumption on the existence of second order moments for errors. Two types of tests for random effects are considered and some new insights are obtained. The proposed tests are distribution-free, though they request the existence of the fourth moments of random effects and errors. The proposed methods are illustrated by simulation studies and a real data analysis.
陈飞博士简介：香港中文大学统计学专业博士（Ph.D.）, 云南财经大学副教授，硕士生导师，主要从事降维理论、统计诊断、结构方程模型、半参数贝叶斯理论等领域的研究工作。
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