Robust nonlinear data smoothers: Definitions and recommendations
AUTOR(ES)
Velleman, Paul F.
RESUMO
Nonlinear data smoothers provide a practical method of finding smooth traces for data confounded with possibly long-tailed or occasionally “spikey” noise. While they are natural tools for analyzing time-series data, they can be applied to any data set for which a sequencing order can be established. Their resistance to the effects of unsupported extreme observations and their ability to respond rapidly to well-supported patterns make them valuable as tools for finding patterns not constrained to specific parametric form and as versatile data-cleaning algorithms. This paper defines some robust nonlinear smoothers that have performed well in Monte-Carlo trials and makes brief recommendations based upon that study.
ACESSO AO ARTIGO
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=392303Documentos Relacionados
- Training in data definitions improves quality of intensive care data
- Robust singular value decomposition analysis of microarray data
- On the estimation of robust stability regions for nonlinear systems with saturation
- MANNGA: A Robust Method for Gap Filling Meteorological Data
- Improving mortality of coronary surgery: Data analysis was not robust