Highest Probability Density Conformal Regions

Max Sampson; Kung-Sik Chan

doi:10.48550/arxiv.2406.08366

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Highest Probability Density Conformal Regions

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Open access

Highest Probability Density Conformal Regions

Max Sampson and Kung-Sik Chan

arXiv.org

Cornell University

06/12/2024

DOI: 10.48550/arxiv.2406.08366

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https://doi.org/10.48550/arxiv.2406.08366View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

We propose a new method for finding the highest predictive density set or region using signed conformal inference. The proposed method is computationally efficient, while also carrying conformal coverage guarantees. We prove that under, mild regularity conditions, the conformal prediction set is asymptotically close to its oracle counterpart. The efficacy of the method is illustrated through simulations and real applications.

Statistics - Computation

Statistics - Methodology

Details

Title: Subtitle: Highest Probability Density Conformal Regions
Creators: Max Sampson
Kung-Sik Chan
Resource Type: Preprint
Publication Details: arXiv.org
DOI: 10.48550/arxiv.2406.08366
eISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 06/12/2024
Academic Unit: Statistics and Actuarial Science; Radiology
Record Identifier: 9984643652602771

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