>>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, let me know. I'm interested in the AIC.
Check out 'Akaike Information Criterion Statistics', 1986, by Sakamoto, Ishiguro, and Kitagawa (who are working associates to Akaike himself). KTK Scientific Publishers, Tokyo. There is an English translation distributed by Kluwer. >If I have 3 models each one fitted with a least square method, are them suitable for AIC application? Yes if the models have different number of free parameters, they have an additive stochastic component, and this component distributes normally. >Are their SSRs the correct ones to use in the AIC? Not quite. Compute the log likelihood under the normal assumption for each model and use that in the AIC. If both the mean and variance of the normal stochastic component are unknown, the log likelihood is L(mu,sigma^2)= -(n/2)ln(2*pi*sigma^2)-(1/2sigma^2)SUM_n(x_i-mu)^2 By taking the partial derivative of the log likelihood with respect to mu and sigma^2, making it zero, solving for the MLE of mu and sigma^2, and replacing these solutions into L, you get the maximum log likelihood of each model, L(mu_hat,sigma^2_hat)=-(n/2)ln(2*pi*sigma^2_hat)-n/2 =-(n/2)ln[(2*pi/n)SUM_n(x_i-mu_hat)^2]-n/2 Note that mu_hat would be each one of your models. Cheers Rub�n http://webmail.udec.cl -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED] with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list * Support to the list is provided at http://www.ai-geostats.org
