Preprint
Flexible Conformal Highest Predictive Conditional Density Sets
arXiv.org
Cornell University
06/26/2024
DOI: 10.48550/arxiv.2406.18052
Abstract
We introduce our method, conformal highest conditional density sets (CHCDS),
that forms conformal prediction sets using existing estimated conditional
highest density predictive regions. We prove the validity of the method and
that conformal adjustment is negligible under some regularity conditions. In
particular, if we correctly specify the underlying conditional density
estimator, the conformal adjustment will be negligible. When the underlying
model is incorrect, the conformal adjustment provides guaranteed nominal
unconditional coverage. We compare the proposed method via simulation and a
real data analysis to other existing methods. Our numerical results show that
the flexibility of being able to use any existing conditional density
estimation method is a large advantage for CHCDS compared to existing methods.
Details
- Title: Subtitle
- Flexible Conformal Highest Predictive Conditional Density Sets
- Creators
- Max SampsonKung-Sik Chan
- Resource Type
- Preprint
- Publication Details
- arXiv.org
- Publisher
- Cornell University; Ithaca, New York
- DOI
- 10.48550/arxiv.2406.18052
- eISSN
- 2331-8422
- Language
- English
- Date posted
- 06/26/2024
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9984649154702771
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