R help - Dec 2004 - How can I estimate parameters of probability distributions?

Hi list, 

I have a group of data. It looks like they follow a exponential
distribution. In R, how can I esimate lamda, that is the rate in pexp,
of the distribution and can I use Kolmogorov-Smirnov for hypothesis
testing in such a situation? I have read the "8.2 Examing the
distribution of a set of data" of "An Introduction to R" but I
did not
find any clues on this issue.  (The data is in the attachment).

Thanks

Wuming
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Hi,

To estimate parameters of exponential distribution you
could use the Maximun Likelihood methods. Find the
log-likelihood function and maximaze it(or minimaze
-log-likelihood function that's the same) calculating
the derivate respect to lamda.

See:

http://www.weibull.com/LifeDataWeb/maximum_likelihood_estimation_exp.htm

Another way is to estimate lamda as 1/mean of sample.

If you use KS test to test if your data belong from an
exponential distribution you're assuming that the
lamda parameter in population is rather equal to the
estimate in sample. I mean that in this way you are
testing both the kind of the distribution and its
parameter lamda.

I hope I give a little help.

Best
Vito



you wrote:

Hi list, 

I have a group of data. It looks like they follow a
exponential
distribution. In R, how can I esimate lamda, that is
the rate in pexp,
of the distribution and can I use Kolmogorov-Smirnov
for hypothesis
testing in such a situation? I have read the "8.2
Examing the
distribution of a set of data" of "An Introduction to
R" but I did not
find any clues on this issue.  (The data is in the
attachment).

Thanks

Wuming

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