Varying coefficient partially functional linear regression models

By relaxing the linearity assumption in partial functional linear regression models, we propose a varying coefficient partially functional linear regression model (VCPFLM), which includes varying coefficient regression models and functional linear regression models as its special cases. We study the...

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Podrobná bibliografie
Vydáno v:	Statistical papers (Berlin, Germany) Ročník 57; číslo 3; s. 827 - 841
Hlavní autoři:	Peng, Qing-Yan, Zhou, Jian-Jun, Tang, Nian-Sheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2016 Springer Nature B.V
Témata:	Approximation Coefficients Convergence Economic Theory/Quantitative Economics/Mathematical Methods Economics Finance Insurance Management Mathematical functions Mathematical models Mathematics and Statistics Operations Research/Decision Theory Parameter estimation Parameters Polynomials Probability Theory and Stochastic Processes Random variables Regression Regression analysis Regular Article Splines Statistical methods Statistics Statistics for Business Studies Global convergence rate Functional linear models Uniform convergence rate Polynomial spline Varying coefficient model
ISSN:	0932-5026, 1613-9798
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Popis
Shrnutí:	By relaxing the linearity assumption in partial functional linear regression models, we propose a varying coefficient partially functional linear regression model (VCPFLM), which includes varying coefficient regression models and functional linear regression models as its special cases. We study the problem of functional parameter estimation in a VCPFLM. The functional parameter is approximated by a polynomial spline, and the spline coefficients are estimated by the ordinary least squares method. Under some regular conditions, we obtain asymptotic properties of functional parameter estimators, including the global convergence rates and uniform convergence rates. Simulation studies are conducted to investigate the performance of the proposed methodologies.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0932-5026 1613-9798
DOI:	10.1007/s00362-015-0681-3