Ga. Almasy et B. Uhrin, PRINCIPLES OF GROSS MEASUREMENT ERROR IDENTIFICATION BY MAXIMUM-LIKELIHOOD-ESTIMATION, Hungarian journal of industrial chemistry, 21(4), 1993, pp. 309-317
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.