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Correlation Analysis in Contaminated Data by Singular Spectrum Analysis
Author(s) -
Rodrigues Paulo Canas,
Mahmoudvand Rahim
Publication year - 2016
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2027
Subject(s) - bivariate analysis , singular spectrum analysis , nonparametric statistics , correlation , statistics , multivariate statistics , data mining , computer science , noise (video) , mathematics , multivariate analysis , algorithm , artificial intelligence , singular value decomposition , geometry , image (mathematics)
Correlation analysis is one of the standard and most informative descriptive statistical tools when studying relationships between variables in bivariate and multivariate data. However, when data is contaminated with outlying observations, the standard Pearson correlation might be misleading and result in erroneous outcomes. In this paper, we propose three new approaches to find linear correlation based on the nonparametric method designed to analyse time series data, the singular spectrum analysis. In these proposals, the correlation is obtained after removing the noise from the data by using singular spectrum analysis based methods. The usefulness of our proposals in contaminated data is assessed by Monte Carlo simulation with different schemes of contamination, and with applications to real data on aluminium industry and synthetic sparse data. In addition, the model comparisons are made with robust hybrid filtering methods. Copyright © 2016 John Wiley & Sons, Ltd.