I'll be in the lab in roughly 45 minutes, so I'll boot Frothie back up. Egads, no one can access my 394 website!! What will they do??
R _____________________________________________________ Motorcycles are everywhere! Check twice; save a life. Rob Keefe Lab: (208) 885-5165 M.S. student Home: (208) 882-9749 University of Idaho _____________________________________________________ On Thu, 17 Jul 2003, Carlos J. Gil Bellosta wrote: > Well, > > If k is known, you can use maximun likelihood to fit the weights, means, > and sd's. The EM algorithm can be of help to solve the optimization > problem. You would have to implement it yourself for your particular > case, but I do not think it is big trouble. > > Then you could estimate k using Bayesian formalism: from a reasonable a > priory distribution on k=1, 2,... compute the posterior distributions > using the densities obtained above, etc. > > Carlos J. Gil Bellosta > Sigma Consultores Estad�sticos > http://www.consultoresestadisticos.com > > Joke Allemeersch wrote: > > > Hello, > > > > I have a concrete statistical question: > > I have a sample of an univariate mixture of an unknown number (k) of > > normal distributions, each time with an unknown mean `m_i' and a > > standard deviation `k * m_i', where k is known factor constant for all > > the normal distributions. (The `i' is a subscript.) > > Is there a function in R that can estimate the number of normal > > distributions k and the means `m_i' for the different normal > > distributions from a sample? Or evt. a function that can estimate the > > `m_i', when the number of distributions `k' is known? > > So far I only found a package, called `normix'. But at first sight it > > only provides methods to sample from such distributions and to > > estimate the densities; but not to fit such a distribution. > > Can someone indicate where I can find an elegant solution? > > > > Thank you in advance > > > > Joke Allemeersch > > > > Katholieke universiteit Leuven. > > Belgium. > > > > ______________________________________________ > > [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 > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
