# Directing Power Towards Conic Parameter Subspaces

## TL;DR

This paper introduces a new high-dimensional test statistic for specific parameter subspaces that improves power and avoids inverse covariance estimation, applicable to sparse or nearly-sparse vectors.

## Contribution

It proposes a novel conic subspace-based test statistic that generalizes Wald tests and is computationally feasible with regularized regression methods.

## Key findings

- Effective in high-dimensional settings with small subspaces
- Compatible with $\, ext{l}_0$- and $ ext{l}_1$-regularized regression
- Demonstrates improved power over traditional quadratic tests

## Abstract

For a high-dimensional parameter of interest, tests based on quadratic statistics are known to have low power against subsets of the parameter space (henceforth, parameter subspaces). In addition, they typically involve an inverse covariance matrix which is difficult to estimate in high-dimensional settings. I simultaneously address these two issues by proposing a novel test statistic that is large in a conic parameter subspace of interest. This test statistic generalizes the Wald statistic and nests many well-known test statistics. For a given parameter subspace, the statistic is free of tuning parameters and suitable for high-dimensional settings if the subspace is sufficiently small. It can be computed using regularized linear regression, where the type of regularization and the regularization parameters are completely determined by the parameter subspace of interest. I illustrate the statistic on subspaces that consist of sparse or nearly-sparse vectors, for which the computation corresponds to $\ell_0$- and $\ell_1$-regularized regression, respectively.