On 28 Aug 2003 at 8:06, Roger Koenker wrote: But is it worth it to modify Kolmogorov-Smirnof fot estimated parameters? It has very low power anyhow. If the null hypothesis is "exponential distributio" (which is a scale family), what about using the quantile transformation twice
new <- qnorm(pexp(old)) to transform from exponential to normal distribution and the applying shapiro.test ? Kjetil Halvorsen > > On Thu, 28 Aug 2003, Prof Brian Ripley wrote: > > > You appear to be applying the KS test after estimating parameters. The > > distribution theory is for an iid sample from a known continuous > > distribution (and does not otherwise depend on the distribution). Since > > your H_0 is not pre-specified, that distribution theory is not correct. > > (Some corrections have been worked out for say ML fitting of exponential > > and normal distributions -- by Michael Stephens as I recall.) > > Just to amplify this comment a bit, I'm a little worried that the > current documentation of of ks.test may make it appear that estimated > parameters are ok, or that somehow the p-values computed are > "corrected" in some way for their existence -- which I very much > doubt. The standard reference on this sort of thing was Durbin's (1973) > SIAM monograph. There is a very nice approach due to Khmaladze (1981) > based on the Doob-Meyer decomposition - this is the closest thing > that I'm aware of for handling KS type tests with estimated parameters > in a general context. > > url: www.econ.uiuc.edu/~roger/my.html Roger Koenker > email [EMAIL PROTECTED] Department of Economics > vox: 217-333-4558 University of Illinois > fax: 217-244-6678 Champaign, IL 61820 > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
