基于高维极值型统计量的非参数条件均值检验

IF 0.8 4区 数学 Q3 STATISTICS & PROBABILITY Scandinavian Journal of Statistics Pub Date : 2023-12-02 DOI:10.1111/sjos.12697
Yiming Liu, Guangming Pan, Guangren Yang, Wang Zhou
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引用次数: 0

摘要

我们提出了一种新的检验方法来研究高维体系中响应变量与相应协变量之间的条件平均相关性。检验统计量是建立在非参数方法上的极值型统计量。建立了在轻度混合条件下所提出的极值型统计量的极限零分布。此外,为了使一般结构的检验更有效,我们提出了一个更一般的检验统计量,并发展了它的渐近性质。本文还考虑了两种方法的功率分析。在实际数据分析中,我们也提出了一种基于我们的结果进行特征筛选的新方法。为了评估我们的估计器和其他方法的性能,进行了大量的模拟。
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Nonparametric conditional mean testing via an extreme-type statistic in high dimension
We propose a new test to investigate the conditional mean dependence between a response variable and the corresponding covariates in the high dimensional regimes. The test statistic is an extreme-type one built on the nonparametric method. The limiting null distribution of the proposed extreme type statistic under a mild mixing condition is established. Moreover, to make the test more powerful in general structures we propose a more general test statistic and develop its asymptotic properties. The power analysis of both methods is also considered. In real data analysis, we also propose a new way to conduct the feature screening based on our results. To evaluate the performance of our estimators and other methods, extensive simulations are conducted.
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来源期刊
Scandinavian Journal of Statistics
Scandinavian Journal of Statistics 数学-统计学与概率论
CiteScore
1.80
自引率
0.00%
发文量
61
审稿时长
6-12 weeks
期刊介绍: The Scandinavian Journal of Statistics is internationally recognised as one of the leading statistical journals in the world. It was founded in 1974 by four Scandinavian statistical societies. Today more than eighty per cent of the manuscripts are submitted from outside Scandinavia. It is an international journal devoted to reporting significant and innovative original contributions to statistical methodology, both theory and applications. The journal specializes in statistical modelling showing particular appreciation of the underlying substantive research problems. The emergence of specialized methods for analysing longitudinal and spatial data is just one example of an area of important methodological development in which the Scandinavian Journal of Statistics has a particular niche.
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