Open AccessComparison of Some Non-Parametric Quality Control Methods
Author(s) -
Hiba Mustafa Fawzi,
Asmaa Ghalib Jaber
Publication year - 2021
Publication title -
mağallaẗ al-ʿulūm al-iqtiṣādiyyaẗ wa-al-idāriyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2518-5764
pISSN - 2227-703X
DOI - 10.33095/jeas.v27i130.2210
Subject(s) - multivariate statistics , kernel principal component analysis , parametric statistics , wilcoxon signed rank test , principal component analysis , nonparametric statistics , computer science , rank (graph theory) , kernel (algebra) , multivariate analysis , statistics , mathematics , data mining , artificial intelligence , kernel method , support vector machine , mann–whitney u test , combinatorics
Multivariate Non-Parametric control charts were used to monitoring the data that generated by using the simulation, whether they are within control limits or not. Since that non-parametric methods do not require any assumptions about the distribution of the data. This research aims to apply the multivariate non-parametric quality control methods, which are Multivariate Wilcoxon Signed-Rank ( ) , kernel principal component analysis (KPCA) and k-nearest neighbor ( −