PRINCIPLES OF GROSS MEASUREMENT ERROR IDENTIFICATION BY MAXIMUM-LIKELIHOOD-ESTIMATION

New theoretical bases are proposed to localize and estimated gross pro cess measurement errors (GE) subject to linear constraints, applying t he Maximum Likelihood (ML) principle. GE itself is considered as rando m variable and two families of distribution are proposed as models. Th e first, more adequate model is the family of Gamma distributed GE-s, the second, less adequate but more practical, is that of the non-zero mean Gaussian GE-s. The concept of GE situations is introduced and the problem of GE identification is formulated as a mixed discrete-contin uous ML estimation to find the actual situation. Algorithm and results of simulation experiments will be given in another paper.