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Detection of gross erros in data reconciliation by principal component analysis
Author(s) -
Tong Hongwei,
Crowe Cameron M.
Publication year - 1995
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690410711
Subject(s) - principal component analysis , statistics , identification (biology) , statistical hypothesis testing , mathematics , component (thermodynamics) , class (philosophy) , data mining , principal (computer security) , econometrics , computer science , artificial intelligence , operating system , botany , physics , biology , thermodynamics
Statistical testing provides a tool for engineers and operators to judge the valididty of process measurements and data reconciliation. Univeriate, maximum power and chisquare tests have been widely used for this purpose. Their performance, however, has not always been satisfactory. A new class of test statistics for detection and identification of gross errors is presented based on principal component analysis and is compared to the other statistics. It is shown that the new test is capable of detecting gross erros of smallmaginitudes and has substantial power to correctly identify the variables in error, when the other tests fail.