Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0167-9473(02)00125-1
Title: Jump process for the trend estimation of time series
Authors: Zhao, S. 
Wei, G.W. 
Keywords: Gaussian kernel
Jump process
Nonparametric regression
The smoothness-fidelity tradeoff
Time series
Trend estimation
Weighted average form
Issue Date: 19-Feb-2003
Citation: Zhao, S., Wei, G.W. (2003-02-19). Jump process for the trend estimation of time series. Computational Statistics and Data Analysis 42 (1-2) : 219-241. ScholarBank@NUS Repository. https://doi.org/10.1016/S0167-9473(02)00125-1
Abstract: A jump process approach is proposed for the trend estimation of time series. The proposed jump process estimator can locally minimize two important features of a trend, the smoothness and fidelity, and explicitly balance the fundamental tradeoff between them. A weighted average form of the jump process estimator is derived. The connection of the proposed approach to the Hanning filter, Gaussian kernel regression, the heat equation and the Wiener process is discussed. It is found that the weight function of the jump process approaches the Gaussian kernel, as the smoothing parameter increases. The proposed method is validated through numerical applications to both real data analysis and simulation study, and a comparison with the Henderson filter. © 2002 Elsevier Science B.V. All rights reserved.
Source Title: Computational Statistics and Data Analysis
URI: http://scholarbank.nus.edu.sg/handle/10635/104802
ISSN: 01679473
DOI: 10.1016/S0167-9473(02)00125-1
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