Kolmogorov-Smirnof test for lognormal distribution with estimated parameters

Hi,

I believe that to performe KS test parameters must not
be estimated by sample data.

Despite some advantages, the KS test has several
important limitations:

   1. It only applies to continuous distributions.
   2. It tends to be more sensitive near the center of
the distribution than at the tails.
-->3. Perhaps the most serious limitation is that the
distribution must be fully specified. That is, if
location, scale, and shape parameters are estimated
from the data, the critical region of the K-S test is
no longer valid. It typically must be determined by
simulation. <--

Due to limitations 2 and 3 above, many analysts prefer
to use the Anderson-Darling goodness-of-fit test.
However, the Anderson-Darling test is only available
for a few specific distributions. 

See:

http://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm

for KS test

http://www.itl.nist.gov/div898/handbook/eda/section3/eda35e.htm
for Anderson-Darling test

http://www.itl.nist.gov/div898/handbook/eda/section3/eda35f.htm
X2 test

I suggest you to use th chisquare test.

Hoping to help you.
Best,
Vito

Hello all,

Would somebody be kind enough to show me how to do a
KS test in R for a lognormal distribution with
ESTIMATED parameters. The R function ks.test()says
"the parameters specified must be prespecified and not
estimated from the data" Is there a way to correct
this when one uses
estimated data?

Regards,

Kwabena.

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