Robust online signal extraction from multivariate time series

Authors:
Vivian Lanius;Ursula Gather
Affiliations:
Kavalierstr. 4A, 13187 Berlin, Germany;Department of Statistics, Technische Universität Dortmund, 44221 Dortmund, Germany
Venue:
Computational Statistics & Data Analysis
Year:
2010

Citing 9
Cited 2

A fast algorithm for the minimum covariance determinant estimator

Technometrics
Influence function and efficiency of the minimum covariance determinant scatter matrix estimator

Journal of Multivariate Analysis
The complexity of computing the MCD-estimator

Theoretical Computer Science
High breakdown estimators for principal components: the projection-pursuit approach revisited

Journal of Multivariate Analysis
Modified repeated median filters

Statistics and Computing
Online signal extraction by robust linear regression

Computational Statistics
The multivariate least-trimmed squares estimator

Journal of Multivariate Analysis
Repeated median and hybrid filters

Computational Statistics & Data Analysis
Nonlinear filtering of multivariate images under robust error criterion

IEEE Transactions on Image Processing

Editorial: Special issue on variable selection and robust procedures

Computational Statistics & Data Analysis
Robust exponential smoothing of multivariate time series

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

Robust regression-based online filters for multivariate time series are proposed and their performance in real time signal extraction settings is discussed. The focus is on methods that can deal with time series exhibiting trends, level changes, outliers and a high level of noise as well as periods of a comparatively steady state. The new filter is based on a robust two-step online procedure, and it recognises that the data are often measured on a discrete scale. The relevant properties and the performance of this new filter are discussed and investigated by means of simulations and by a medical application.