[R] Goodness of fit test for estimated distribution

[roger koenker]

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	rkoenker at uiuc.edu			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
> hennig at math.uni-hamburg.de, http://www.math.uni-hamburg.de/home/hennig/
> #######################################################################
> ich empfehle www.boag-online.de
>
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