On Sun, 13 Aug 2000, AJ wrote:
> I'm having trouble in choosing the right method to analyse a large
> dataset. I have N data, consisting of measured responses fn(t) all of
> the same length T.
This sounds as though you have a N-by-T data matrix: N cases or
observations (as rows in the matrix), and T variables (as columns).
If you had something else in mind, my subsequent remarks may verge on
nonsense. Using "T" leads one to wonder whether the data in fact form a
time series?
> Each fn(t) can be considered to have one part, fsame(t), that is the
> same throughout the dataset and another that is varying,
> fn_different(t).
In other words, for fn(t), t = 1,...,T, fn(t) = constant for all N rows,
t = 1,...,(say) k; and fn(t) varies from row to row, t = k+1,...,T ?
Do you know in advance which t are associated with constant fn(t) and
which t are associated with varyhing fn(t)? Or is that part of what is
to be inferred from the data?
> I'm interested in performing some sort of correlation/statistical
> analysis of the data, that can tell me how the part of the data in
> fn(t), that are varying (ie. fn_different(t)) are dependent of
> each other, ie. are the parts statistically independent or not, and if
> not with which distribution do they depend of each other or ?
This sounds as though you want to know what correlational structure
exists among the (T-k) variables fn(t), t = k+1,...T. What kinds of
models of relationships among variables are you interested in
considering? What particular models are important to you?
I'm not proposing any answers to your questions, because I'm unsure
whether my perception of your data is anything at all like yours.
I'd suspect that anyone else would have similar difficulties, although
some contributors to this list might be willing to make assumptions
about the parts of your question that I've found ambiguous. Please
respond to the list, not just to me; there are several possible things
that you might be wanting to do for which others are considerably more
skillful than I am.
-- DFB.
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