Data validation in the presence of imprecisely known correlations

This paper derives fundamental results for data validation in the presence of imprecisely known correlations. Given a constraint on the maximum absolute correlation of a given estimate and measurement data, a tight upper bound for the joint covariance matrix is derived, which finally yields a modified Mahalanobis distance. The special cases of one-dimensional and two-dimensional random variables are discussed.