Problems
EC
regulation n°3127/94 states 3 statistical constraints. ·
The method for assessing the lean meat content of
pig carcasses has to be either double regression or other statistically proven
procedure. ·
The resulting precision is at least equal to that
obtained using standard regression techniques on 120 carcasses. ·
Authorisation of the grading methods shall,
moreover, be subject to the root mean square deviation of the errors, measured
about zero, being less than 2,5. In
case of double regression, formulas to check the two last constraints have been
developed, but they need a wider diffusion within the EU. Another technique,
called regression with surrogate predictors, has been imagined in order to
reduce experimental cost. But the second constraint has to be explained. Due to
increasing complexity of the measuring procedures other techniques like
principal components regression (PCR) or partial least squares (PLS) have been
used. For these methods the two last constraints have not been demonstrated in
particular the criterion root mean square deviation of the errors is not
adequate. In
case of very large numbers of measurements collected per carcass it is also
interesting to reduce experimental cost. Constraints are not explicit for any
combination of all these methods. Other
methods, like for instance, project pursuit regression, multivariate calibration, krigeage or neural network seem to be potentially
interesting, but
also an explanation of the constraints does not exist.

Objective
The
objective of this task is to enable the EU countries to respect the constraints
of the EU regulations about pig classification on the same scientific basis.
Furthermore, to make it possible to use the most advanced statistical techniques
in the correct and cheapest way. It is therefore very important to solve the
present problems, to anticipate the future problems and to form a basis for the
evolution of the regulations. Work
in progress In
order to manage both outliers and influential observations, robust estimation
seems to be an efficient tool. A
study and description of all relevant statistical methods for estimating
predictions formulas are in progress. Comparison between OLS and PLS has been
done. Data from Autofom have been processed by PLS using SAS software. Some
works concern the criteria to validate the estimations and to assess the
predictive abilities. It is proposed to use the average of Mean Squared
Prediction Error (MSPE) where the average is over a random sample from the
national population. Specific attention is paid to bias.
