Abstract
AbstractStatistical 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.
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