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An information criterion for robust estimation with unnormalized statistical models
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AI
DOI：10.1007/s10463-025-00963-8.png)
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Abstract
En 中文
Noise contrastive estimation (NCE) is a popular approach for parameter estimation of unnormalized statistical models. NCE is based on a maximum likelihood estimation framework for a classification task, which makes the parameter estimation of unnormalized models sensitive to outlier noise included in the dataset. To cope with this problem, a robust version of NCE called gamma\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}-NCE (GNCE) has been proposed. In this paper, we investigate asymptotic statistical properties of GNCE and propose an information criterion for GNCE, which enables us to select an appropriate model even when the dataset is contaminated by outlier noise.
Keywords:
Unnormalized model
Contrastive estimation
Robustness
Information criterion
Journal
A
IF:
0.6
Papers:
26
Citations:
2.1K