# A Flexible Framework for Hypothesis Testing in High-dimensions

## TL;DR

This paper introduces a versatile framework for hypothesis testing in high-dimensional linear regression, enabling valid inference for complex hypotheses when the number of parameters exceeds samples, with proven error control and high power.

## Contribution

It develops a general testing framework for high-dimensional models that handles various hypotheses and provides minimax optimal confidence intervals for linear functionals.

## Key findings

- Controls false positive rate near nominal level
- Demonstrates high statistical power in experiments
- Provides minimax rate optimal confidence intervals

## Abstract

Hypothesis testing in the linear regression model is a fundamental statistical problem. We consider linear regression in the high-dimensional regime where the number of parameters exceeds the number of samples ($p> n$). In order to make informative inference, we assume that the model is approximately sparse, that is the effect of covariates on the response can be well approximated by conditioning on a relatively small number of covariates whose identities are unknown. We develop a framework for testing very general hypotheses regarding the model parameters. Our framework encompasses testing whether the parameter lies in a convex cone, testing the signal strength, and testing arbitrary functionals of the parameter. We show that the proposed procedure controls the type I error, and also analyze the power of the procedure. Our numerical experiments confirm our theoretical findings and demonstrate that we control false positive rate (type I error) near the nominal level, and have high power. By duality between hypotheses testing and confidence intervals, the proposed framework can be used to obtain valid confidence intervals for various functionals of the model parameters. For linear functionals, the length of confidence intervals is shown to be minimax rate optimal.