Let $X\geq 0$ be a real random variable and $h:\mathbb{R} \rightarrow \mathbb{R}$ a monotonously growing, continuously differentiable function with $h(0)=0$.
Show:
$E[h(X)] = \int_0^{\infty} h'(t)P[X>t]dt$
With the given hint I got
$E[h(X)] = \int_0^{\infty} \int_{0}^x h'(t)dt dF(x)$
with Fubinis theorem I can change the order of integration to
$\int_0^{\infty} \left( \int_{t}^{\infty} dF(x)\right)h'(t)dt$
which gives me
$\int_0^{\infty} P[X>t]h'(t)dt$
How would I go about finding the first two moments in integral form like above?
I got this far:
first moment
$E[X] = \int_0^{\infty} x F(x) dx = \int_0^{\infty}\int_0^{x}dx F(x) dt = \int_0^{\infty}\int_t^{\infty} F(x) dx dt = \int_0^{\infty}P[X>t]dt$
second moment
$E[X^2] = \int_0^{\infty} x^2 F(x) dx = \int_0^{\infty}\int_0^{x}2x F(x) dx dt = 2\int_0^{\infty}\int_t^{\infty} x F(x) dx dt$
But now what? $\int_t^{\infty}x dx$ doesn't converge.