[ADMB Users] parameterization of Cholesky factor

[dave fournier]

Actually using the choleski decomp to parameterize the covariance matrix 
is an old idea.

see Pinheiro and Bates.

http://scholar.google.ca/scholar_url?hl=en&q=ftp://netlib.bell-labs.com/cm/ms/departments/sia/project/nlme/Parametrizations.ps&sa=X&scisig=AAGBfm1_xR0qo8qRKtNN4lEoPa6KZy0Yag&oi=scholarr&ei=x3RnVO3kJMjvigKtiYH4CQ&ved=0CBsQgAMoADAA

But what is new in my approach is the particular parameterization used 
for the choleski decomp of the correlation
matrix. P and B suffer from an inability to deal with dependent 
variables.  This is the common R failing which
permeates the entire R approach to nonlinear parameter estimation.

I note that P and b remark that there are multiple choleski decomps of 
the covariance matrix leading to the same
covariance matrix and this may cause difficulties.  I believe that by 
starting with the correlation matrix and
then scaling it by   the std devs parameterized on the log scale leads 
to a unique parameterization.  So I think the entire thing
is superior to anything P and B did.

I suppose I should have published it but reviewers suck so bad they ruin 
my day.