Thank you. I actually found fitdistr() in the package MASS, that "estimates" the df, but it does a very bad job. I know that the main problem is that the t distribution has a lot of local maxima, and of course, when k->infty we have the Normal distribution, which has nice and easy to obtain MLEs.
I will try re-parametrizing k, but I doubt this will solve the problem with the multiple local maxima. I would like to implement something like the EM algorithm to go around this problem, but I don't know how to do that. Barbara On Thu, Dec 10, 2009 at 2:59 PM, Albyn Jones <jo...@reed.edu> wrote: > k -> infinity gives the normal distribution. You probably don't care > much about the difference between k=1000 and k=100000, so you might > try reparametrizing df on [1,infinity) to a parameter on [0,1]... > > albyn > > On Thu, Dec 10, 2009 at 02:14:26PM -0600, Barbara Gonzalez wrote: >> Given X1,...,Xn ~ t_k(mu,sigma) student t distribution with k degrees >> of freedom, mean mu and standard deviation sigma, I want to obtain the >> MLEs of the three parameters (mu, sigma and k). When I try traditional >> optimization techniques I don't find the MLEs. Usually I just get >> k->infty. Does anybody know of any algorithms/functions in R that can >> help me obtain the MLEs? I am especially interested in the MLE for k, >> the degrees of freedom. >> >> Thank you! >> >> Barbara >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.