I think that I had found the original post from a Canadian group.

Besides Peter McDonald and his contribution to the MIX software, I found out a Juan Du Master’s student’s thesis. And there is a mixdist R package

I am amazed to find out that mixture of models have been intensively study by so many researchers and for so many years! David Dowe. It is often an paradox, how much deep can an individual go into. I guess that is how an person can become an expert.

Now, with the most superficial approach, I need to clear out some basic road blocks:

Detail properties of normal and gamma distribution

How gamma becomes a normal distribution

Chi-square test on goodness-of-fit and degree of freedom

With the general MIX program, it fits a set of data with “mixparameters” and proposed “kernels”, then it come back with a fit to the “histogram”, with chi-square test on the fitting. It should report parameters of those distributions that make up the mixture data. It might sounds like a good approach:

With a set of data coming from a mixture distributions

Fit with MIX or mixdist and assess the fit with Chi-square test

Pick whichever winners and extract the parameters from those distributions

Then, restore the mixture distribution with known parameters (and/or the proportions??)

In the end, take the derivatives (second) and finish the data transformation

The next topic will be SVM or any other clustering procedures for modeling and building the prediction model.