Oracally efficient spline smoothing of nonlinear additive autoregression models with simultaneous confidence band |
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Authors: | Qiongxia Song |
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Institution: | Michigan State University, East Lansing, MI 48824, USA |
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Abstract: | Under weak conditions of smoothness and mixing, we propose spline-backfitted spline (SBS) estimators of the component functions for a nonlinear additive autoregression model that is both computationally expedient for analyzing high dimensional large time series data, and theoretically reliable as the estimator is oracally efficient and comes with asymptotically simultaneous confidence band. Simulation evidence strongly corroborates with the asymptotic theory. |
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Keywords: | primary 62G08 62G15 secondary 62G05 62G20 |
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