Re: Gaussian distribution and likelihood question
- From: "zl2k" <kdsfinger@xxxxxxxxx>
- Date: 30 Jan 2007 07:06:57 -0800
On Jan 29, 7:28 pm, David Winsemius <doe_s...@xxxxxxxxxxx> wrote:
"zl2k" <kdsfin...@xxxxxxxxx> wrote in news:1170093960.945613.276350
@p10g2000cwp.googlegroups.com:
hi,
My question is: is there any method to measure how a data set fits a
Gaussian model? Suppose I have one dataset A which has N elements and
another dataset B which has M elements. From each of the dataset I can
estimate one Gaussian model, thus I have (mu1, sigma1) for A and (mu2,
sigma2) for B. How can I tell which dataset fits the Gaussian model
better? (skewness and kurtosis does not tell about fitness)
What I am thinking is from the Gaussian model I can generate the same
amount of elements and those elements should fit the model best. Then
I can compare the log-likelihood of the real dateset to the generated
dataset. But, to produce a psudo dataset from a Gaussian model is not
efficient. My next question is: is there a 1-step formula to get the
log-likelihood of the "theorically perfect Gaussian dataset" with N
elements? Basically, find the most possible log-likelihood of N
elements from Gaussian model(mu, sigma).Suggest a quantile-quantile plot.
--
David Winsemius
Thanks David. The Q-Q plot needs to iterate on each element which is
not feasible in my application. I am looking for some way that does
not need to produce the pseudo data from the model. (Since the pseudo
data is produced from a known model, I guess everything I want to get
from the psuedo data is already there, hidden in the known model. Then
maybe I do not need to touch the pseudo data. I am not sure.)
zl2k
.
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