Representations based on zero-crossings in scale-space
Readings in computer vision: issues, problems, principles, and paradigms
Computational Statistics & Data Analysis
Semiparametric approaches to signal extraction problems in economic time series
Computational Statistics & Data Analysis
On spline estimators and prediction intervals in nonparametric regression
Computational Statistics & Data Analysis
Estimation of the conditional distribution in regression with censored data: a comparative study
Computational Statistics & Data Analysis
Improving nonparametric regression methods by bagging and boosting
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Forecasting of the electric energy demand trend and monthly fluctuation with neural networks
Computers and Industrial Engineering
On the robust detection of edges in time series filtering
Computational Statistics & Data Analysis
Robust estimation of multivariate jump-diffusion processes via dynamic programming
Proceedings of the Winter Simulation Conference
| Hi-index | 0.03 |
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.