# Adaptive estimation of the sparsity in the Gaussian vector model

## TL;DR

This paper develops adaptive methods for estimating and testing the sparsity level in Gaussian vector models, providing minimax optimal procedures and extending to unknown variance scenarios.

## Contribution

It introduces a new adaptive testing procedure for sparsity estimation and a novel framework for assessing estimator optimality in Gaussian models.

## Key findings

- Established minimax separation distances for sparsity testing.
- Proposed a minimax adaptive test for sparsity.
- Provided a new approach to evaluate the optimality of sparsity estimators.

## Abstract

Consider the Gaussian vector model with mean value {\theta}. We study the twin problems of estimating the number |{\theta}|_0 of non-zero components of {\theta} and testing whether |{\theta}|_0 is smaller than some value. For testing, we establish the minimax separation distances for this model and introduce a minimax adaptive test. Extensions to the case of unknown variance are also discussed. Rewriting the estimation of |{\theta}|_0 as a multiple testing problem of all hypotheses {|{\theta}|_0 <= q}, we both derive a new way of assessing the optimality of a sparsity estimator and we exhibit such an optimal procedure. This general approach provides a roadmap for estimating the complexity of the signal in various statistical models.