This really depends to a large degree on the associated
scientific and practical problem, which is not clear
to me. But in principle I strongly support the view
implied by Maarten Buis: only bounded distributions are
appropriate for finite intervals. What's more their
behaviour at their extremes should surely be compatible,
without jumps and ideally without kinks too, i.e. [10,20]
should join [20,30].
Whatever your problem is, it is difficult to believe
that there is not a literature on it, e.g. in demography,
actuarial science, population ecology.
Nick
[email protected]
Reza C Daniels
I've found a solution to the uniform distribution in the
-egen var=seq()
from() to()- command.
Is it not simpler just to try and transform this into the three
appropriate normal and skewed distributions than to use the -betaden-
set of commands? If so, how? If not, I revert to below.
I'm not sure I'm getting the intuition behind the code of the beta
density functions -betaden- and -nbetaden-. My reading
suggests using
-betaden- for the symmetric ~ about 25, and -nbetaden- for
the skewed ~s
about 22.5 & 27.5.
However, when I plug in the numbers I get a single result.
Clearly I'm
doing something very wrong. Does this mean I need to
calculate a & b &
lambda (shape paramaters in betaden commands) first somehow?
Maarten Buis
you can have a look at the beta distribution
a normal distribution will never stay within an interval (except
[minus infinity, plus infinity])
Reza C Daniels
I have a categorical variable for agegroup in 10 year
bands (e.g. 20-30
years old). I would like to convert the categorical age
variable to a
continuous variable by imposing various distributions on
the range of
each interval. I then want to conduct sensitivity analysis to my
distributional assumptions.
For example: let a = the lower limit and b = upper limit
for each age
group (e.g. a= 20 years old, b= 30 years old). Keeping the [20,30]
example, the four distributions I want to examine are:
1) Uniformly distributed over [20,30].
2) Normally distributed on the closed interval [20,30],
with mode at 25.
3) Positively skewed on the closed interval [20,30], with
mode at 22.5.
4) Negatively skewed on the closed interval [20,30], with
mode at 27.5.
I have tried various commands (including -drawnorm-), but
am unable to
control my variance to ensure the tails are bounded by
[20,30] in the
example above (generically, the interval [a,b]).
Any suggestions on the code for all four distributions
above would be
very much appreciated.
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