For a random vector $(X,Y)$ characterized by a copula $C_{X,Y}$ we study its perturbation $C_{X+Z,Y}$ characterizing the random vector $(X+Z,Y)$ affected by a noise $Z$ independent of both $X$ and $Y$. Several examples are added, including a new comprehensive parametric copula family ${\left({𝒞}_k\right)}_{k\in [-\infty ,\infty ]}$.

In many applications, high-dimensional problem may occur often for various reasons, for example, when the number of variables under consideration is much bigger than the sample size, i.e., p >> n. For highdimensional data, the underlying structures of certain covariance matrix estimates are usually blurred due to substantial random noises, which is an obstacle to draw statistical inferences. In this paper, we propose a method to identify the underlying covariance structure by regularizing...