On Thu, 3 Nov 2011, bonda wrote:
Thank you. I've understood, that it should be k (number of parameters)
separate Brownian bridges.
Well, if you use a process based on OLS residuals, you have always a
one-dimensional process even though your model has k parameters. Hence,
the two parameters are really conceptually different..
Is it possible, to get such separated/disaggregated processes also in
function efp()? (one can take gefp(..., family=gaussian), or construct by
myself residuals(lm.model)*X, but still interesting).
Some processes that efp() computes are always 1-dimensional (namely those
based on residuals) while some are k-dimensional (namely the
estimates-based processes) and some are (k+1)-dimensional (the score-based
processes).
gefp() generalizes this concept and lets you construct the fluctuation
processes fairly flexibly.
And on the contrary, how can I get an aggregated Brownian bridge path
for all parameters together, similar to efp()$process? It is made in
plot.gefp, but only for graphical visualization...
For "gefp" objects all aggregation is done by the efpFunctional employed.
But this is really described in a fair amount of detail in the
accompanying papers. Specifically, for gefp/efpFunctional in the 2006 CSDA
paper.
Thank you in advance!
Julia
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