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Show that the set of strictly stationary, mean zero and finite variance stochastic processes is closed (or not)

Let $\mathcal{P}$ be the set of real-valued and strictly stationary processes with expectation zero and finite variance, i.e.: \begin{equation} \mathcal{P}:=\left\{ X = (X_t)_{t \in \mathbb{Z}} \, ...
Fam's user avatar
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Is the topology generated by the convergence of finite-dimensional distributions metrizable?

Let $\mathbf{D} := D([0,1]; \mathbb{R}^d)$ be the Skorokhod space (equipped with the Skorokhod metric) of càdlàg functions, and let $X = (X_t)_{t \geq 0}$ be its canonical process. The space of ...
vaoy's user avatar
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2 votes
2 answers
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non-homogeneous counting process

Consider a counting process $\{N(t), t\geq 0\}$ where the time distribution between any two consecutive events, say $k$ and $k+1$ has a Poisson rate $\lambda(k)$, which is an explicit function of $k$....
user86217's user avatar