功能线性量回归模型的一致性规范检验

IF 0.3 4区 数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY Statistics and Its Interface Pub Date : 2024-07-19 DOI:10.4310/22-sii754
Lili Xia, Zhongzhan Zhang, Gongming Shi
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引用次数: 0

摘要

本文主要研究函数线性量回归模型的规格检验。根据残差的正交性及其条件期望,提出了一种非参数检验统计量。在温和的假设条件下,证明了所提出的统计量在零假设条件下近似服从标准正态分布,但在备择假设条件下则趋于无穷大。对于一些局部替代假设,还给出了检验的渐近功率。该检验易于实现,即使样本量较小,通过模拟也能显示出其强大的功能。为了便于说明,我们还提供了一个使用 Capital Bikeshare 数据的真实数据示例。
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A consistent specification test for functional linear quantile regression models
This paper is focused on the specification test of functional linear quantile regression models. A nonparametric test statistic is proposed based on the orthogonality of residual and its conditional expectation. It is proved with mild assumptions that the proposed statistic follows asymptotically the standard normal distribution under the null hypothesis, but tends to infinity under alternative hypothesis. The asymptotic power of the test is also presented for some local alternative hypotheses. The test is easy to implement, and is shown by simulations powerful even for small sample sizes. A real data example with the Capital Bikeshare data is presented for illustration.
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来源期刊
Statistics and Its Interface
Statistics and Its Interface MATHEMATICAL & COMPUTATIONAL BIOLOGY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
0.90
自引率
12.50%
发文量
45
审稿时长
6 months
期刊介绍: Exploring the interface between the field of statistics and other disciplines, including but not limited to: biomedical sciences, geosciences, computer sciences, engineering, and social and behavioral sciences. Publishes high-quality articles in broad areas of statistical science, emphasizing substantive problems, sound statistical models and methods, clear and efficient computational algorithms, and insightful discussions of the motivating problems.
期刊最新文献
Estimating extreme value index by subsampling for massive datasets with heavy-tailed distributions Default Bayesian testing for the zero-inflated Poisson distribution A consistent specification test for functional linear quantile regression models Variable selection and estimation for high-dimensional partially linear spatial autoregressive models with measurement errors A double regression method for graphical modeling of high-dimensional nonlinear and non-Gaussian data
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