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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Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Vol. 57; no. 3; pp. 827 - 841
Main Authors: Peng, Qing-Yan, Zhou, Jian-Jun, Tang, Nian-Sheng
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2016
Springer Nature B.V
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-015-0681-3