Inference without compatibility: Using exponential weighting for inference on a parameter of a linear model

Michael Law; Yaacov Ritov

doi:10.3150/20-bej1280

Inference without compatibility: Using exponential weighting for inference on a parameter of a linear model

Michael Law
, Yaacov Ritov

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

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 vn-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.

Original language	English
Pages (from-to)	1467-1495
Number of pages	29
Journal	Bernoulli
Volume	27
Issue number	3
DOIs	https://doi.org/10.3150/20-bej1280
State	Published - Aug 2021
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2021 International Statistical Institute. All rights reserved.

Keywords

Compatibility Condition
Exponential Weighting
Inference
Lasso

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10.3150/20-bej1280

Cite this

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abstract = "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 vn-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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Inference without compatibility: Using exponential weighting for inference on a parameter of a linear model

Abstract

Bibliographical note

Keywords

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