An optimal-time algorithm for slope selection
SIAM Journal on Computing
Finding the repeated median regression line
SODA '92 Proceedings of the third annual ACM-SIAM symposium on Discrete algorithms
Introduction to Algorithms
Computing the update of the repeated median regression line in linear time
Information Processing Letters
Modified repeated median filters
Statistics and Computing
Image denoising via gradient approximation by upwind scheme
Signal Processing
Editorial: 2nd Special Issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
On the robust detection of edges in time series filtering
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Robust online signal extraction from multivariate time series
Computational Statistics & Data Analysis
LIBRAS translator via web for mobile devices
Proceedings of the 6th Euro American Conference on Telematics and Information Systems
On robust cross-validation for nonparametric smoothing
Computational Statistics
| Hi-index | 0.03 |
Standard median filters preserve abrupt shifts (edges) and remove impulsive noise (outliers) from a constant signal but they deteriorate in trend periods. Finite impulse response median hybrid (FMH) filters are more flexible and also preserve shifts, but they are much more vulnerable to outliers. Application of robust regression, in particular of the repeated median, has been suggested for removing subsequent outliers from a signal with trends. A fast algorithm for updating the repeated median in linear time using quadratic space has been given by Bernholt and Fried (Inform. Process. Lett. 88 (2003) 111). Repeated median hybrid filters are constructed to combine the robustness of the repeated median with the edge preservation of FMH filters. Analytical properties of these filters are investigated and their performance is compared via simulations. An algorithm for updating the repeated median is presented which needs only linear space.