Suppose $N\left(\cdot\right)$ is a Poisson Process with rate $1$ and $Z$ is a positive non-constant random variable, define $N_{Z}\left(t\right)=N\left(Zt\right)$. I know that conditional on $Z$ this is a Poisson Process with rate $Z=z$ but without conditioning while it has stationary increments they are not independent. However I don't know how to show that they are not independent which is what I would like to do.
Following the comment, does it suffice to show that given $t_{0}<t_{1}<t_{2}$ $$\mathbb{P}\left(\left\{ N_{Z}\left(t_{1}\right)-N_{Z}\left(t_{0}\right)=0\,\wedge\, N_{Z}\left(t_{2}\right)-N_{Z}\left(t_{1}\right)=0\,|Z=z\right\} \right)\neq\mathbb{P}\left(N_{Z}\left(t_{1}\right)-N_{Z}\left(t_{0}\right)=0|Z=z\right)\mathbb{P}\left(N_{Z}\left(t_{2}\right)-N_{Z}\left(t_{1}\right)=0|Z=z\right)$$
Generally to show that there is no independence I would want to show that $$\mathbb{P}\left(\left\{ N_{Z}\left(t_{1}\right)-N_{Z}\left(t_{0}\right)=0\,\wedge\, N_{Z}\left(t_{2}\right)-N_{Z}\left(t_{1}\right)=0\right\} \right)\neq\mathbb{P}\left(N_{Z}\left(t_{1}\right)-N_{Z}\left(t_{0}\right)=0\right)\mathbb{P}\left(N_{Z}\left(t_{2}\right)-N_{Z}\left(t_{1}\right)=0\right)$$ I'm not sure I understand why it suffices to show it after conditioning on $Z$?
Following Ben's response I still have some questions:
I don't immediately see why $\mathbb{P}\left[N\left(Z\right)=0\right]=\mathbb{E}\left[e^{-Z}\right]$ and the same for the other equalities.
I'm not sure in what part exactly are we conditioning on $Z$, like you said conditioned on Z I won't get the desired result since $N|Z$ is a proper Poisson Process.
I'm also not sure why showing there's no equality in the last equation yoou wrote would give me what I want. Generally as far as I understood what I want to do is take two non-overlapping intervals and show the increments of $N_{Z}\left(\cdot\right)$ in those intervals are not independent.
I would extremely appreciate if you could clear this out for me, it's been a while since I took a course in Probability and I'm a bit lost now in this Random Processes course as a result.. :)