🚀 go-pugleaf

Okay-- I've read the recommended chapters.  I still have
a question which I'm certain is easy for most of you using
AR/MA/ARMA or almost anything from Marple's book.  Stephen
Oh (and Marple) mention in passing that p (or sometimes, ip),
the dimension of the autocorrelation matrix, must be less
than N, the dimension of the 1D dataset.  In fact, Oh suggest
that p<=16, if N=64, and says that "it is known that" this is
true.  Marple says only that p<<N.  What's going on here?

When I learned about autocorrelations, the autocorrelation
vector was either considered to be the same dimension as the
dataset (i.e., p=N), or it was padded with zeroes (p>N).

Can someone point me to a reference which explains the rationale?
Or maybe brain me with a brief yet brilliant explanation?
Is this related to work with "infinite" (i.e., continuously acquired)
datasets?

Theo Smit: if you are reading this, I would appreciate it if you
could send me your EMail address-- the address which shows up in
your postings doesn't work very well-- I'd like to know a little more
about your experiences with ARMA and NMR spectroscopy (thanks!).

Thanks, one and all!

Jim Kohli
GE Medical Systems
We Bring Dog Things To Life
uucp: {your favorite path to uunet}!uunet!crdgw1!gemed!hal!kohli
internet: kohli@hal.med.ge.com