Re: AI-GEOSTATS: Akaike's information criterion (AIC)

2002-12-18 Thread Brian R Gray
REML = variously, restricted or residual ML. the kicker is that, under REML, a function of the outcomes are estimated, such that the function contains none of the fixed effects present/suspected in the original outcomes. brian Bri

Re: AI-GEOSTATS: Akaike's information criterion (AIC)

2002-12-18 Thread Ruben Roa
>>The algebraic expression for the AIC results from the bias in the maximum log-likelihood of a model as estimator of the mean expected log-likelihood, this bias being a function of the number of free parameters in the model. So it only covers those models fitted by maximum likelihood. > >Please, l

Re: AI-GEOSTATS: Akaike's information criterion (AIC)

2002-12-18 Thread Ruben Roa
>suspect Ruben would note that, under a normal assumption, OLS and ML coincide. True, though i'd say that OLS is a particular case of MLE iff the process being modelled is additive and the additive stochastic component is normal. >also, I suspect that Ruben's comments also apply to REML >results-

Re: AI-GEOSTATS: Fractal analysis of monitoring networks?

2002-12-18 Thread Syed Abdul Rahman Shibli
On 18/12/02 1:01 PM, "Gregoire Dubois" <[EMAIL PROTECTED]> wrote: > Dear all, > > I'm looking for a Windows software able to perform a 2D computation (either > sandbox or box counting) of the fractal dimension of a monitoring network. The > method is illustrated in You can assume a fractional i

Re: AI-GEOSTATS: Akaike's information criterion (AIC)

2002-12-18 Thread Brian R Gray
suspect Ruben would note that, under a normal assumption, OLS and ML coincide. also, I suspect that Ruben's comments also apply to REML results--altho in that case you may need to restrict inference to random components. brian Bri

Re: AI-GEOSTATS: Akaike's information criterion (AIC)

2002-12-18 Thread Claudio Cocheo
Dear all, The algebraic expression for the AIC results from the bias in the maximum log-likelihood of a model as estimator of the mean expected log-likelihood, this bias being a function of the number of free parameters in the model. So it only covers those models fitted by maximum likelihood.

Re: AI-GEOSTATS: Akaike's information criterion (AIC)

2002-12-18 Thread Ruben Roa
>Dear all, > >The AIC is used to select the "best" model from a list >of theoretical functions. I wonder if its necessary >the models need to be fitted by the same method ? Yes. The model must be fitted my maximum likelihood. >Would it be possible to stress the AIC to select the >"best" model fr

AI-GEOSTATS: Summary: indicator kriging with a trend

2002-12-18 Thread Marius Gilbert
Dear Colleagues, Two responses to my post to the list concerning indicator kriging in the presence of a trend were posted to the list by Isobel Clark and Pierre Goovaerts, and will not be repeated here. I got a third response from Donald Myers (pasted at the bottom of this message), complementi

AI-GEOSTATS: Fractal analysis of monitoring networks?

2002-12-18 Thread Gregoire Dubois
Dear all, I'm looking for a Windows software able to perform a 2D computation (either sandbox or box counting) of the fractal dimension of a monitoring network. The method is illustrated in Lovejoy S., D. Schertzer and P. Ladoy (1986). Fractal characterization of inhomogeneous geophysical meas

AI-GEOSTATS: Akaike's information criterion (AIC)

2002-12-18 Thread vanessa stelzenmüller
Dear all, The AIC is used to select the "best" model from a list of theoretical functions. I wonder if its necessary the models need to be fitted by the same method ? Would it be possible to stress the AIC to select the "best" model from models which were fitted for example by OLS,WLS, REML etc.