Online signal extraction by robust linear regression

被引：31

作者：

Gather, U ^{[1
]}

Schettlinger, K

Fried, R

机构：

[1] Univ Dortmund, Dept Stat, D-44221 Dortmund, Germany

[2] Univ Carlos III Madrid, Dept Stat, Madrid 28903, Spain

来源：

COMPUTATIONAL STATISTICS | 2006年 / 21卷 / 01期

关键词：

robust filtering; least median of squares; least trimmed squares; repeated median; deepest regression; breakdown point;

D O I：

10.1007/s00180-006-0249-8

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 [统计学]; 070103 [概率论与数理统计]; 0714 [统计学];

摘要：

In intensive care, time series of vital parameters have to be analysed online, i.e. without any time delay, since there may be serious consequences for the patient otherwise. Such time series show trends, slope changes and sudden. level shifts, and they axe overlaid by strong noise and many measurement artefacts. The development of update algorithms and the resulting increase in computational speed allows to apply robust regression techniques to moving time windows for online signal extraction. By simulations and applications we compare the performance of least median of squares, least trimmed squares, repeated median and deepest regression for online signal extraction.

引用

页码：33 / 51

页数：19

共 25 条

[1]

[Anonymous], 1987, ROBUST REGRESSION OU

[2]

Bai ZD, 1999, ANN STAT, V27, P1616

[3]

Computing the update of the repeated median in linear time regression line [J].