  In this paper, we provide a proof for the Hanson-Wright inequalities for
sparsified quadratic forms in subgaussian random variables. This provides
useful concentration inequalities for sparse subgaussian random vectors in two
ways. Let $X = (X_1, \ldots, X_m) \in \mathbb{R}^m$ be a random vector with
independent subgaussian components, and $\xi =(\xi_1, \ldots, \xi_m) \in \{0,
1\}^m$ be independent Bernoulli random variables. We prove the large deviation
bound for a sparse quadratic form of $(X \circ \xi)^T A (X \circ \xi)$, where
$A \in \mathbb{R}^{m \times m}$ is an $m \times m$ matrix, and random vector $X
\circ \xi$ denotes the Hadamard product of an isotropic subgaussian random
vector $X \in \mathbb{R}^m$ and a random vector $\xi \in \{0, 1\}^m$ such that
$(X \circ \xi)_{i} = X_{i} \xi_i$, where $\xi_1, \ldots,\xi_m$ are independent
Bernoulli random variables. The second type of sparsity in a quadratic form
comes from the setting where we randomly sample the elements of an anisotropic
subgaussian vector $Y = H X$ where $H \in \mathbb{R}^{m\times m}$ is an $m
\times m$ symmetric matrix; we study the large deviation bound on the
$\ell_2$-norm of $D_{\xi} Y$ from its expected value, where for a given vector
$x \in \mathbb{R}^m$, $D_{x}$ denotes the diagonal matrix whose main diagonal
entries are the entries of $x$. This form arises naturally from the context of
covariance estimation.
