Brian Ripley sometimes on this list or elsewhere suggested to
reparametrize as 1/k. I have used that with good results. But you
should be aware that
usually data contains very little information about k, so thhat
if you do not have a lot more than 100 observations you coukld
be out of luck. You sho
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.
k -> infinity gives the normal distribution. You probably don't care
much about the difference between k=1000 and k=10, 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
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 anybod
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