In article <a145qk$qfq$[EMAIL PROTECTED]>,
Chia C Chong <[EMAIL PROTECTED]> wrote:
>Hi!
>I have a series of observations of 2 random variables (say X and Y) from my
>measurement data. These 2 RVs are not independent and hence f(X,Y) ~=
>f(X)f(Y). Hence, I can't investigate f(X) and f(Y) separately. I tried to
>plot the 2-D kernel density estimates of these 2 RVs and from the it looks
>like Laplacian/Gaussian/Generalised Gaussian shape in one side and the other
>side looks like Gamma/Weibull/Exponential shape.
>My intention is to find the joint 2-D distribution of these 2 RVs so that I
>can reprenseted this by an equation (so that I could regenerate this plot
>using simulation later on). I wonder whether anyone has come across this
>kind of problem and what method that I should use??
There is, in the collection by Johnson and Kotz (and others
for some of the volumes), a listing of "classical" bivariate
distributions. It is hard enough to estimate one-dimensional
distributions; it gets worse as the dimension increases.
--
This address is for information only. I do not claim that these views
are those of the Statistics Department or of Purdue University.
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
[EMAIL PROTECTED] Phone: (765)494-6054 FAX: (765)494-0558
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