🚀 go-pugleaf

Hello,

A question about which critera to use when doing experimental planning.

A test plan that can be used without 'a priori' knowledge about 
the model structure 
is a plan with as low correlation between the independent variables as
possible. 

In the literature this type of plan is perhaps considered to simple
for discussion.

I would like to get some pointers to literature stating that a minimum
correlated testplan is a good approach  if the model structure is UNKNOWN.

A group of people here at my laboratory is talking in favor of using
a critera of the following kind:

   maximum ( determinant( transpose(X) * X ) ) 

 where X is a ( m * n ) matrix with m experiment and n variables.

It can be shown that this method minimizes the coovariance of the
parameterestimates. 

BUT if this criteria is used there can exist coovariances between
undependent variables up to 0.2 or 0.3 in our examples, compared to
a max of 0.04 when using  

 minimum(  biggest element in coovariance matrix  )

as criteria.


MY QUESTIONS:

1. References or comments on giving the coovariance between independent 
   variables in a testplan high priority ?

2. What's Your criteria when planning tests for unknown modelstructures? 



Post or mail, whatever You prefer.
I will summarize Your comments if they are not posted to the newsgroup.

All comments appreciated,
Staffan

        
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 -               Project researcher / Environmental Engineering             -   
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 -  Staffan Asplund                  -              email sasplund@abo.fi   -
 -  Process Design Lab.              -              phone +358-21-654552    -
 -  Abo Akademi University           -              ph. home  921-882744    -
 -  Finland                          -              fax   +358-21-654791    -
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