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Adaptive estimation for varying coefficient modelswith nonstationary covariates
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Mathematical Statistics)ORCID iD: 0000-0001-5673-620X
2019 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 48, no 16, p. 4034-4050Article in journal (Refereed) Published
Abstract [en]

In this paper, the adaptive estimation for varying coefficient models proposed by Chen, Wang, and Yao (2015) is extended to allowing for nonstationary covariates. The asymptotic properties of the estimator are obtained, showing different convergence rates for the integrated covariates and stationary covariates. The nonparametric estimator of the functional coefficient with integrated covariates has a faster convergence rate than the estimator with stationary covariates, and its asymptotic distribution is mixed normal. Moreover, the adaptive estimation is more efficient than the least square estimation for non normal errors. A simulation study is conducted to illustrate our theoretical results.

Place, publisher, year, edition, pages
Taylor & Francis, 2019. Vol. 48, no 16, p. 4034-4050
Keywords [en]
Varying coefficient model, adaptive estimation, local linear fitting, non stationary covariates
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-154754DOI: 10.1080/03610926.2018.1484483ISI: 000473519800007Scopus ID: 2-s2.0-85059303429OAI: oai:DiVA.org:umu-154754DiVA, id: diva2:1274392
Part of project
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment, Swedish Research Council
Funder
Swedish Research Council, 340-2013-534Available from: 2018-12-30 Created: 2018-12-30 Last updated: 2019-08-06Bibliographically approved

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Zhou, ZhiyongYu, Jun

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