PERBANDINGAN METODE KEKAR BIWEIGHT MIDCOVARIANCE DAN MINIMUM COVARIANCE DETERMINANT DALAM ANALISIS KORELASI KANONIK

Freza Riana, Aji Hamim Wigena, Erfiani .

Abstract


Canonical Correlation Analysis
(CCA) is a multivariate linear used to
identify and quantify associations
between two sets of random variables. Its
standard computation is based on sample
covariance matrices, which are however
very sensitive to outlying observations.
The robust methods are needed. There
are two robust methods, i.e robust
Biweight Midcovariance (BICOV) and
Minimum Covariance Determinant
(MCD) methods. The objective of this
research is to compare the performance
of both methods based on mean square
error. The data simulations are
generated from various conditions. The
variation data consists of the proportion
of outliers, and the kind of outliers: shift,
scale, and radial outlier. The
performance of robust BICOV method in
CCA is the best compared to MCD and
Classic

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