[R] univariate normal mixtures
keef9490 at uidaho.edu
Thu Jul 17 19:13:33 CEST 2003
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On Thu, 17 Jul 2003, Carlos J. Gil Bellosta wrote:
> 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 Estadísticos
> 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.
> > ______________________________________________
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