Respected sir i need complete help on it. I have my model if you would like to apply this method on my model and provide me with the estimates. I am completely unaware of this method and my thesis is to be submitted in few days.
EM is a rather high level paradigm for estimating parameters in statistical models. Unless your model is something standard like mixture of Gaussians, it cannot be used "out of the box". If you have a custom model you basically have to device the expectation and maximization steps and then generate code to iterate them, something that is usually rather complicated: if you have never heard of EM algorithm and only have few days left it is probably impossible.
If you could give more information on the details of the model you have maybe we could give more specific advice.
Well, I doubt there is a ready made package for applying EM to those (though I might be wrong.
To implement the Expectation Maximization-algorithm, you would need to do following things:
first select random parameter values for the distribution (like lambda and k for the weibull). Then
E) assign each data point to the distribution that is most likely to have generated that point given the random parameters.
M) Then calculate new distribution parameters lookiing only at the points assigned to the distribution in the first step.
Now you have new parameter values that you use again in step E to assign each point to the distribution it was likely from. Then you iterate between these steps until some convergence criterion is met (that is, the fit does not get any better). Note that his procedure will only find local optimum, so you want to run it several times with different starting values.