Our exam today contained a few things I wasn't able to compute starting with this:
Let U,V,W,Y,Z be independent random variables with the following distributions
- $U\sim\text{Exp}(1/5)$
- $V\sim\text{Exp}(2/3)$
- $W\sim\mathcal U(1, 5)$ (uniform distribution on $[1;5]$)
- $Y\sim\mathcal N(-13, 1)$
- $Z\sim\mathcal N(0, 5)$
Determine the following expected value: $\mathbb{E}[(Z+\min(U+W,V+W))\cdot(Y+Z)]$
Hint. With a suitable approach you do not need to derive any density or distribution functions by hand. All necessary means can be computed directly via the parameters of the distributions.
As mentioned in the title I failed to deduce $\mathbb{E}[\min(U+W,V+W)]$ while being able to split evrything up based on independence and linearity of expectation. I started with a substitution $T:=U+W$ and $S:=V+W$ such that with convolution I get something like this (not formally correct, just random formulas as there were too much variables for me...)
$\mathbb{E}[\min(U+W,V+W)]=\\=\iint\limits_{-\infty}^\infty\left(\min(t,s)\right)\cdot\underbrace{\left(\int\limits_{-\infty}^\infty 1/5\cdot\exp\left(-1/5\cdot t\right)\cdot 1/4\cdot \mathbb 1_{[1;5]}(t) \right)}_{\text{pdf of }T}\cdot\underbrace{\left(\ldots\right)}_{\text{pdf of }S}\;\mathrm dt\;\mathrm ds$
The most ridiculous thing was $W$ - I haven't had ANY clue at all, how to handle it. Can anyone explain me a more suitable approach?
EDIT: $\mathbb 1_A(x)$ denotes the indicator function which returns 1 if $x\in A$; otherwise, 0.