Hello Julien,
thank you for your reply.
I should have mentioned in first place that I have read the paper on
ANCOVA based on PCE. As far as I understood one neglects there the
(necessary) assumption of independency of the input vectors in the PCE.
I am rather interested in an approach where you define a specific
ordering of the variables and make them independent. Can you provide
me a short example how the implemented Rosenblatt transformation is
supposed to work? Does it only worked the way it is implemented for
some known distributions or incorporates it a kernel density
estimation of a arbitrary data set?
Thank you,
Simon
PS: Sorry for the fragmentary subject
Quoting Julien Schueller | Phimeca <[email protected]>:
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Hello Simon,
Please have a look at the ANCOVA algorithm.
Julien
-------------------------
DE : [email protected] <[email protected]> de la part
de Simon Rittel <[email protected]>
ENVOYé : jeudi 13 juin 2019 17:23:28
À : [email protected]
OBJET : [ot-users] reply: usage of
Hello,
with one of the four implemented "SobolIndicesAlgorithm"s one can
compute the Sobol coefficients for a function with independent inputs.
Am I correct that there's no direct method implemented to calculate
these coefficients for dependent input variables?
To cope with dependent inputs one could apply the well-known
Rosenblatt transformation before the sensitivity analysis. I saw that
there are already isoprobabilistic transformations implemented, but
from the documentation I couldn't figure out how exactly they are
meant to work (the theory, however, I do understand) and therefore
also how to use them. Maybe you could provide me a short example or
explain briefly how to generate a independent input vector with the
help of the implemented Rosenblatt transformation?
I would appreciate your help on these two questions.
Thank you,
Simon Rittel
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