Sharp Decoupling Inequalities for the Variances and Second Moments of Sums of Dependent Random Variables

Both complete decoupling and tangent decoupling are classical tools aiming to compare two random processes where one has a weaker dependence structure. We give a new proof for the complete decoupling inequality, which provides a lower bound for the sum of dependent square-integrable nonnegative random variables ${\sum }_{i=1}^n{d}_i$ $$\frac{1}{2}\mathbb{E}{\left(\sum _{i=1}^n{z}_i\right)}^2\le \mathbb{E}{\left(\sum _{i=1}^n{d}_i\right)}^2,$$ where $z_i{d}_i$ for all $i\le n$ and $z_i$’s are mutually independent. We will then provide the following sharp tangent decoupling inequalities $$\mathbb{V}ar\left(\sum _{i=1}^n{d}_i\right)\le 2\mathbb{V}ar\left(\sum _{i=1}^n{e}_i\right),$$ and $$\mathbb{E}{\left(\sum _{i=1}^n{d}_i\right)}^2\le 2\mathbb{E}{\left(\sum _{i=1}^n{e}_i\right)}^2-{\left[\mathbb{E}\left(\sum _{i=1}^n{e}_i\right)\right]}^2,$$ where $\{e_i\}$ is the decoupled sequences of $\{d_i\}$ and $d_i$’s are not forced to be nonnegative. Applications to construct Chebyshev-type inequality and Paley-Zygmund-type inequality, and to bound the second moments of randomly stopped sums will be provided.