Preprint
On a fast consistent selection of nested models with possibly unnormalised probability densities
ArXiV.org
Cornell University
03/08/2025
DOI: 10.48550/arxiv.2503.06331
Abstract
Models with unnormalized probability density functions are ubiquitous in statistics, artificial intelligence and many other fields. However, they face significant challenges in model selection if the normalizing constants are intractable. Existing methods to address this issue often incur high computational costs, either due to numerical approximations of normalizing constants or evaluation of bias corrections in information criteria. In this paper, we propose a novel and fast selection criterion, T-GIC, for nested models, allowing direct data sampling from a possibly unnormalized probability density function. T-GIC gives a consistent selection under mild regularity conditions and is computationally efficient, benefiting from a multiplying factor that depends only on the sample size and the model complexity. Extensive simulation studies and real-data applications demonstrate the efficacy of T-GIC in the selection of nested models with unnormalized probability densities.
Details
- Title: Subtitle
- On a fast consistent selection of nested models with possibly unnormalised probability densities
- Creators
- Rong Bian - University of Chinese Academy of SciencesKung-Sik Chan - University of IowaBing Cheng - University of Chinese Academy of SciencesHowell Tong - London School of Economics and Political Science
- Resource Type
- Preprint
- Publication Details
- ArXiV.org
- DOI
- 10.48550/arxiv.2503.06331
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 03/08/2025
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9984800197002771
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