  \cite{HillMotegi2017} present a new general asymptotic theory for the maximum
of a random array $\{\mathcal{X}_{n}(i)$ $:$ $1$ $\leq $ $i$ $\leq $
$\mathcal{L}\}_{n\geq 1}$, where each $\mathcal{X}_{n}(i)$ is assumed to
converge in probability as $n$ $\rightarrow $ $\infty $. The array dimension
$\mathcal{L}$ is allowed to increase with the sample size $n$. Existing extreme
value theory arguments focus on observed data $\mathcal{X}_{n}(i)$, and require
a well defined limit law for $\max_{1\leq i\leq
\mathcal{L}}|\mathcal{X}_{n}(i)|$ by restricting dependence across $i$. The
high dimensional central limit theory literature presumes approximability by a
Gaussian law, and also restricts attention to observed data.
\cite{HillMotegi2017} do not require $\max_{1\leq i\leq
\mathcal{L}_{n}}|\mathcal{X}_{n}(i)|$ to have a well defined limit nor be
approximable by a Gaussian random variable, and we do not make any assumptions
about dependence across $i$. We apply the theory to filtered data when the
variable of interest $\mathcal{X}_{n}(i,\theta _{0})$ is not observed, but its
sample counterpart $\mathcal{X}_{n}(i,\hat{\theta}_{n})$ is observed where
$\hat{\theta}_{n}$ estimates $\theta _{0}$. The main results are illustrated by
looking at unit root tests for a high dimensional random variable, and a
residuals white noise test.
