Edge detection using median comparisons
Computer Vision, Graphics, and Image Processing
Nonparametric tests for edge detection in noise
Pattern Recognition
Adaptive L-filters with applications in signal and image processing
Proceedings of of the IEEE winter workshop on Nonlinear digital signal processing
Jump process for the trend estimation of time series
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Identifying outliers with sequential fences
Computational Statistics & Data Analysis
Robust edge detection in noisy images
Computational Statistics & Data Analysis
Implementing a class of structural change tests: An econometric computing approach
Computational Statistics & Data Analysis
Repeated median and hybrid filters
Computational Statistics & Data Analysis
Multilevel nonlinear filters for edge detection and noisesuppression
IEEE Transactions on Signal Processing
Editorial: 2nd Special Issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
Online analysis of time series by the Qn estimator
Computational Statistics & Data Analysis
On the online estimation of local constant volatilities
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
Abrupt shifts in the level of a time series represent important information and should be preserved in statistical signal extraction. Various rules for detecting level shifts that are resistant to outliers and which work with only a short time delay are investigated. The properties of robustified versions of the t-test for two independent samples and its non-parametric alternatives are elaborated under different types of noise. Trimmed t-tests, median comparisons, robustified rank and ANOVA tests based on robust scale estimators are compared.