On Dec 10, 2009, at 10:59 AM, Santosh wrote:
Dear R/Statistics-gurus!
I tried to find answer to my hypothetical question and in vain.
Sorry, I
don't have a dataset that fits into this hypothetical question and
pardon me
if my explanations/use of statistical terms are not accurate.
It does sound a weird question, but I want to rule out that line of
thought.
Is it possible to develop a model (or a simulation) such that the
upper
variability is different from lower variability? e.g, the upper
variability
in the data above a model predicted value may be less than the
variability
in the data below a model predicted value. I guess mixture model is
not
applicable here
Wouldn't any model with Poisson- (and by extension gamma-) distributed
errors satisfy this requirement? (Not to mention models with even
heavier right tails)
Around a population estimate (say, mean or maximum likelihood) one
of the
following may apply:
total standard deviation (SD) = SD(lower) + SD(upper)
total variance (var) = var(lower) + var(upper);
If it is possible, how do I assign variability in parameters and
residual
(additive + proportional) errors?
To fit the observed,
Y = F + (a^2 +b^2/F^2)
F = f(x,Ai, var(Ai)); where Ai = a matrix of parameters; x = a vector
independent variables; var(Ai) = variability in the parameter (Ai)
Regards,
Santosh
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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