[R] Implementation of quasi-bayesian maximum likelihood estimation for normal mixtures

[Helena Richter]

Hi, 

as you can see in the topic, I am trying to fit a normal mixture 
distribution with the approach suggested by Hamilton (1991). Since I 
couldn't find any existing packages including the quasi-bayesian mle, I 
have to write my own function. Unfortunately, I have absolutely no 
experience in doing this.

If you're not familiar with the QB-MLE, I attached the formula as pdf. 
The idea is to extend the usual MLE with prior beliefs about the values 
sigma_n and sigma_b. My priors are already included in the code below. I 
intend to try a mixture of two normal distributions with same mean, and 
variances 1 and 5 as starting values.
This is what I've done so far:

 > R <-read.table("C:\\...\\rendite.txt", header=F)
 > qbmle  <- function(p, data){
    mu <- mean(data);   
(-sum(log(p[1]/p[2]*exp(-0.5*(data-mu)^2/p[2]^2)+(1-p[1])/p[3]*exp(-0.5*data^2/p[3]^2)))-2.772*log(p[2]^2)-2.772*log(p[3]^2) 
- 2.772/p[2]^2 - 13.86/p[3]^2 )}
 > start <-c(0.9, 1, 5)
 > out <- nlm(qbmle, start, data=R)

The result is: error in nlm(...): non-finite value for nlm, plus a lot 
of warnings, and the following output:

 > out
$minimum
[1] -27513.60

$estimate
[1]  3.478212e+04 -2.146767e+03 -3.806269e-02

$gradient
[1] -5.971628e-02  1.939856e-03 -2.946156e+02

$code
[1] 5

$iterations
[1] 49

So, what did I do wrong? How can I implement any non-negative 
constraints, and a restriction for p to be between 0 and 1?
I'm sorry to bother you with such a beginner's question and am very 
helpful for any remarks. I don't have to use the qb-mle so if you think 
there's a better way to do the estimation tell me.

Thanks a lot,
Helena






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