Minimax Lower Bounds for Linear Independence Testing (1601.06259v1)

Abstract: Linear independence testing is a fundamental information-theoretic and statistical problem that can be posed as follows: given $n$ points ${(X_i,Y_i)}^n_{i=1}$ from a $p+q$ dimensional multivariate distribution where $X_i \in \mathbb{R}^p$ and $Y_i \in\mathbb{R}^q$, determine whether $a^T X$ and $b^T Y$ are uncorrelated for every $a \in \mathbb{R}^p, b\in \mathbb{R}^q$ or not. We give minimax lower bound for this problem (when $p+q,n \to \infty$, $(p+q)/n \leq \kappa < \infty$, without sparsity assumptions). In summary, our results imply that $n$ must be at least as large as $\sqrt {pq}/|\Sigma_{XY}|F^2$ for any procedure (test) to have non-trivial power, where $\Sigma{XY}$ is the cross-covariance matrix of $X,Y$. We also provide some evidence that the lower bound is tight, by connections to two-sample testing and regression in specific settings.