PDF of summation of independent random variables with different mean and variances What can we say about the probability distribution function of $n$ independent random variables with different means and variances but the same PDF?
For example, lets say $X_1,X_2,\dots,X_n$ are independent random variables with the following PDFs:
$$
\begin{align}
X_1 & \sim N(μ_1,σ_1) \\
X_2 & \sim N(μ_2,σ_2) \\
& {}\,\vdots \\
X_n & \sim N(μ_n,σ_n)
\end{align}
$$
So they are all Normal random variables with different means and variances and their summation would also have Normal distribution. Now my question is what about other distribution functions?
 A: If $f(x)\,dx$ is a probability distribution with expected value $0$ and variance $1$, and the distribution of $X_i$ is
$$f\left(\dfrac{x-\mu_i}{\sigma_i}\right)\cdot\dfrac{dx}{\sigma_i},$$
and $X_i$ are independent, then certainly the distribution of $X_1+\cdots+X_n$ has expected value $\mu_1+\cdots+\mu_n$ and variance $\sigma_1^2+\cdots+\sigma_n^2$.  Also, the higher cumulants would add together in the same way.  (The fourth cumulant, for example, is $\mathbb E((X-\mu)^4) - 3(\mathbb E((X-\mu)^2))^2$, and the coefficient $3$ is the only number that makes this functional additive in the sense that the fourth cumulant of a sum of independent random variables is the sum of their fourth cumulants.)
We've tacitly assumed $\sigma_i<\infty$.  I think if $\sum_{i=1}^\infty\sigma_i^2=\infty$, then as $n$ grows, the distribution would approach a normal distribution (I'm not recalling the appropriate generalization of the central limit theorem clearly enough to state it precisely.)  But what happens for small $n$ is another question, and the answer would depend on what function $f$ is.
I said above that $f(x)\,dx$ has expectation $0$ and variance $1$.  But one can also have perfectly good location-scale families in which the expectation, and a fortiori, the variance, do not exist.  The most well-known case is the Cauchy distribution.  The simplest result there is that $(X_1+\cdots+X_n)/n$ actually has the same Cauchy distribution as $X_1$ if these $n$ variables are i.i.d.  It doesn't get narrower. So a lot depends on which function $f$ is.
A: It seems the question is to find families $F$ of distributions, other than the gaussian family, such that the distribution of every linear combination of independent random variables with distributions in $F$ is in $F$. The problem is not really well defined hence let us specify things somewhat:

Let $F_h$ denote the family of distributions of random variables $X$ such that $E[\mathrm e^{\mathrm itX}]=\mathrm e^{\mathrm it\mu-h(\sigma t)}$ for some $(\mu,\sigma)$ with $\sigma\geqslant0$. Does it satisfy the property above?

Note that the gaussian case is when $h(t)=\frac12t^2$. The family $F_h$ satisfies the property above if and only if, for every $\sigma_1$ and $\sigma_2$, there exists some $\mu$ and $\sigma$ such that
$$
h(\sigma_1 t)+h(\sigma_2 t)=h(\sigma t)+\mu t.
$$
In particular, stable distributions (for some fixed exponent $\alpha$) spring to mind. 
