Robustifying Likelihoods by Optimistically Re-weighting Data

• 成果类型: Article; Early Access
• 署名作者: Dewaskar, Miheer; Tosh, Christopher; Knoblauch, Jeremias; Dunson, David B.
• 署名单位: University of New Mexico; Memorial Sloan Kettering Cancer Center; University of London; University College London; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2468012
• 发表日期: 2025
• 关键词: robustness; distance; EFFICIENCY; models; distributions; estimators; location

摘要

Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in selecting a good model for the data at hand, there is inevitably some amount of model misspecification: outliers, data contamination or inappropriate parametric assumptions such as Gaussianity mean that most models are at best rough approximations of reality. A significant practical concern is that for certain inferences, even small amounts of model misspecification may have a substantial impact; a problem we refer to as brittleness. This article attempts to address the brittleness problem in likelihood-based inferences by choosing the most model friendly data generating process in a distance-based neighborhood of the empirical measure. This leads to a new Optimistically Weighted Likelihood (OWL), which robustifies the original likelihood by formally accounting for a small amount of model misspecification. Focusing on total variation (TV) neighborhoods, we study theoretical properties, develop estimation algorithms and illustrate the methodology in applications to mixture models and regression. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.