Inference Without Compatibility

Michael Law; Ya'acov Ritov

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arXiv · 1903.06295

Inference Without Compatibility

Michael Law·Ya'acov Ritov

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Abstract

We consider hypotheses testing problems for three parameters in high-dimensional linear models with minimal sparsity assumptions of their type but without any compatibility conditions. Under this framework, we construct the first $\sqrt{n}$-consistent estimators for low-dimensional coefficients, the signal strength, and the noise level. We support our results using numerical simulations and provide comparisons with other estimators.

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Michael Law, Ya'acov Ritov. 2019-03-14. Inference Without Compatibility. https://arxiv.org/abs/1903.06295

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