Suppose I have $X_{n} \sim MVN(\underline{\mu},\Sigma)$ where $n$ is large (several thousands). However, the $\mu_i's$ and the elements of $\Sigma$ are such that almost every simulation from $X_n$ yields a set of numbers that look like a Normal distribution of a specific mean and standard deviation say $Z \sim N(\lambda,\beta)$. Is there a term for $Z$?
For example, using R, I generate 10000 numbers from exp(1/20)
mu <- rexp(10000, 1/20)
Then, I use each of these numbers $\mu_i$ as a mean for one of the components of a MVN. For simplicity, I assume the components are independent and that the variance components are $\mu_i/100$
So, a single vector simulated from this MVN would be:
y<-numeric(10000)
for(i in 1:10000){
y[i]<-rnorm(1,mu[i],mu[i]/100)
}
Now I have a vector y that is one simulation from my MVN. What I am interested in is that when I look at the values in the y vector together (the 10000 components of the vector), they then resemble a univariate distribution.
Here is a sample graphic where I have three simulations of the y vector plotted as density curves. My question is that is it reasonable to study this curve and approximate it using some univariate distribution?