Ma. Stapanian et al., FINDING SUSPECTED CAUSES OF MEASUREMENT ERROR IN MULTIVARIATE ENVIRONMENTAL DATA, Journal of chemometrics, 7(3), 1993, pp. 165-176
Environmental data are usually multivariate, with the variables confor
ming to some correlation structure. Occasionally, measurements which d
o not conform in structure or magnitude may occur in one or more varia
bles. It is important (1) to characterize these discordancies in terms
of the disturbed variables and the direction and magnitude of the ano
malous error and (2) to associate each discordant observation with a s
pecific cause of measurement error in order to prevent further mismeas
urement. We describe a procedure for identifying suspected causes of d
iscordant observations in otherwise multinormal data sets. Variables a
re assigned to groups, each of which is associated with a specific cau
se of measurement error. Discordant observations are identified with t
he generalized distance test or the multivariate kurtosis test. Suspec
ted causes of measurement error are identified by repeating the tests
with one of the groups of variables omitted in each analysis. The proc
edures are evaluated with simulated data sets having a correlation str
ucture similar to that of a large environmental data set.