Linear hypothesis testing in ultra high dimensional generalized linear mixed models

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Xiyun Zhang, Zaixing Li
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引用次数: 0

Abstract

This paper is concerned with linear hypothesis testing problems in ultra high dimensional generalized linear mixed models where the response and the random effects are distribution-free. The constrained-partial-regularization based penalized quasi-likelihood method is proposed and the corresponding statistical properties are studied. To test linear hypotheses, we propose a partial penalized quasi-likelihood ratio test, a partial penalized quasi-score test, and a partial penalized Wald test. The theoretical properties of these three tests are established under both the null and the alternatives. The finite sample performance of the proposed tests has been shown by the simulation studies, and the forest health data is illustrated by our procedure.

Abstract Image

超高维广义线性混合模型中的线性假设检验
本文关注响应和随机效应无分布的超高维广义线性混合模型中的线性假设检验问题。本文提出了基于约束-部分正规化的惩罚准似然法,并研究了其相应的统计特性。为了检验线性假设,我们提出了部分受惩罚的准似然比检验、部分受惩罚的准分数检验和部分受惩罚的 Wald 检验。这三种检验的理论性质是在零检验和替代检验下建立的。通过模拟研究证明了所提出检验的有限样本性能,并通过我们的程序对森林健康数据进行了说明。
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来源期刊
Journal of the Korean Statistical Society
Journal of the Korean Statistical Society 数学-统计学与概率论
CiteScore
1.30
自引率
0.00%
发文量
37
审稿时长
3 months
期刊介绍: The Journal of the Korean Statistical Society publishes research articles that make original contributions to the theory and methodology of statistics and probability. It also welcomes papers on innovative applications of statistical methodology, as well as papers that give an overview of current topic of statistical research with judgements about promising directions for future work. The journal welcomes contributions from all countries.
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