Articles

Document Type: Journal article (JA)
Title: Model checks for functional linear regression models based on projected empirical processes
Author: Chen, Feifei (1); Jiang, Qing (2); Feng, Zhenghui (3); Zhu, Lixing (4, 5)
作者单位: (1) School of Statistics, Renmin University of China, Beijing, China (2) School of Statistics, Southwestern University of Finance and Economics, Chengdu, China (3) MOE Key Laboratory of Econometrics, Department of Statistics, School of Economics, and the Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China (4) Department of Mathematics, Hong Kong Baptist University, Hong Kong (5) School of Statistics, Beijing Normal University, Beijing, China
RPAddress: Xiamen Univ, Sch Econ, Dept Stat, MOE Key Lab Econometr, Xiamen, Peoples R China.; Feng, ZH (reprint author), Xiamen Univ, Wang Yanan Inst Studies Econ, Xiamen, Peoples R China.
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Journal: Computational Statistics and Data Analysis
Publisher: Elsevier B.V.
ISSN: 0167-9473
Published: 2020-04, 144:-.
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Fund_Code: National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11871409, 11671042, 11971064]; Humanity and Social Science Youth Foundation of Ministry of Education of China [18YJC910006]; Fundamental Research Funds for the Central Universities, ChinaFundamental Research Funds for the Central Universities [JBK1805004]; Joint Lab of Data Science and Business Intelligence at Southwestern University of Finance and Economics, China; University Grants Council of Hong Kong
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Keywords: Ergonomics; Monte Carlo methods; Regression analysis; Statistical tests
Abstract: The goodness-of-fit testing for functional linear regression models with functional responses is studied. A residual-marked empirical process-based test is proposed. The test is projection-based, which can well circumvent the curse of dimensionality. The test is omnibus against any global alternative hypothesis as it integrates over all projection directions in the unit ball. The weak convergence of the test statistic under the null hypothesis is derived and it is shown that the proposed test can detect the local alternative hypotheses distinct from the null hypothesis at the fastest possible rate of order O(n?12). To reduce computational burden for critical value determination, a nonparametric Monte Carlo method is used, and simulation studies show the good performance of the proposed method in various scenarios. An ergonomics data set is analyzed for illustration. ? 2019 Elsevier B.V.
WOS Categories: Computer Science, Interdisciplinary Applications; Statistics & Probability
Accession Number: WOS:000515446200031
EI收录号: 20195107876775
DOI: 10.1016/j.csda.2019.106897
ESI_Type: MATHEMATICS
Collection: SCIE, EI

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