This may not be as satisfying for some purposes as a clean, theoretical result, but it produced useful answers without busting the project budget too badly.
hope this helps. spencer graves
roger koenker wrote:
In full generality this is a quite difficult problem as discussed in Durbin's (1973) SIAM monograph. An elegant general approach is provided by Khmaladze
@article{Khma:Arie:1981,
author = {Khmaladze, E. V.},
title = {Martingale approach in the theory of goodness-of-fit tests},
year = {1981},
journal = {Theory of Probability and its Applications (Transl of Teorija Verojatnostei i ee Primenenija)},
volume = {26},
pages = {240--257}
}
but I don't think that there is a general implementation of the approach for R, or
any other software environment, for that matter.
url: www.econ.uiuc.edu/~roger Roger Koenker email [EMAIL PROTECTED] Department of Economics vox: 217-333-4558 University of Illinois fax: 217-244-6678 Champaign, IL 61820
On Jun 29, 2004, at 1:08 PM, Christian Hennig wrote:
Hi,
is there any method for goodness of fit testing of an (as general as
possible) univariate distribution with parameters estimated, for normal,
exponential, gamma distributions, say (e.g. the corrected p-values for
the Kolmogorov-Smirnov or Chi-squared with corresponding ML estimation
method)?
It seems that neither ks.test nor chisq.test handle estimated parameters.
I am aware of function goodfit in package vcd, which seems to it for some
discrete distributions.
Thank you for help, Christian
*********************************************************************** Christian Hennig Fachbereich Mathematik-SPST/ZMS, Universitaet Hamburg [EMAIL PROTECTED], http://www.math.uni-hamburg.de/home/hennig/ ####################################################################### ich empfehle www.boag-online.de
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