Adaptive orthogonal series estimation in additive stochastic regression models
In this paper, we consider additive stochastic nonparametric regression models. By approximating the nonparametric components by a class of orthogonal series and using a generalized cross-validation criterion, an adaptive and simultaneous estimation procedure for all the nonparametric components is constructed. We illustrate the adaptive and simultaneous estimation procedure by a number of simulated and real examples.
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|Item Type:||Journal Article|
|Keywords:||Adaptive estimation, additive model, dependent process, mixing condition, nonlinear time series, nonparametric regression, orthogonal series, strict stationarity, truncation parameter|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > PURE MATHEMATICS (010100)|
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Australian and New Zealand Standard Research Classification > ECONOMICS (140000) > ECONOMETRICS (140300)
|Divisions:||Current > QUT Faculties and Divisions > QUT Business School|
|Copyright Owner:||Copyright 2002 Institute of Statistical Science, Academia Sinica|
|Copyright Statement:||Reproduced in accordance with the copyright policy of the publisher.|
|Deposited On:||16 Jan 2007|
|Last Modified:||05 Jan 2011 23:29|
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