Institution profile

University of Oslo

Academic institutioneurope · no
Official website
Research library376linked papers
Opportunities0open roles
Selected work

Representative Papers

Confidence distributions and related themes

Oct 01, 2017

This work addresses the long-standing lack of a systematic theoretical foundation for confidence distributions (CDs) within the frequentist framework, which has hindered their broader adoption. By integrating and extending the theoretical underpinnings of CDs—drawing on Fisher’s concept of fiducial likelihood and modern frequentist inference techniques—the project establishes a unified framework for parametric inference. Through the systematic development of its methodology and promotion of interdisciplinary applications, the study not only strengthens the theoretical basis of CDs but also revitalizes their role in statistical practice. The findings synthesize cutting-edge research presented at the 2015 “Inference With Confidence” workshop in Oslo, significantly enhancing the influence and practical utility of confidence distributions in the statistical community.

19 citationsRead paper

Optimal inference via confidence distributions for two-by-two tables modelled as Poisson pairs: fixed and random effects

Feb 20, 2026

This study addresses the challenge of sparse 2×2 contingency tables in medical meta-analyses caused by rare events, where existing methods often yield unreliable inferences. The authors propose a novel unified inference framework that integrates confidence distributions with Poisson-pair modeling, enabling optimal estimation of treatment effects and their ratios under both fixed- and random-effects models. By introducing Poisson-pair modeling into confidence-distribution-based meta-analysis for the first time, the method substantially enhances the accuracy and reliability of statistical inference in rare-event settings. Empirical evaluation on real-world datasets demonstrates its superior performance compared to conventional approaches.

3 citationsRead paper

The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks

Mar 29, 2024arXiv.org

Lexical Semantic Change Detection (LSCD) has long suffered from severe heterogeneity in datasets, preprocessing pipelines, and evaluation metrics, impeding fair model comparison and reproducibility. Method: We introduce the first modular, plug-and-play standardized LSCD benchmark platform that unifies evaluation protocols across three hierarchical tasks—Word-in-Context (WiC), Word Sense Induction (WSI), and LSCD. Our approach innovatively models lexical usage evolution as a graph structure, integrating cross-temporal semantic clustering, sense induction, and context-aware word sense disambiguation. All components are open-sourced with full implementation transparency. Contribution/Results: The framework significantly improves evaluation consistency and reproducibility, enables independent assessment of subtasks and joint optimization, and has emerged as the de facto community standard for LSCD research.

3 citationsRead paper

Examining marginal properness in the external validation of survival models with squared and logarithmic losses

Dec 10, 2022

This paper addresses the theoretical validity of two widely used external validation metrics for survival analysis models—Integrated Survival Brier Score (ISBS) and Right-Censored Log-Likelihood (RCLL)—by introducing “marginal propriety” as a novel formal criterion for scoring rule appropriateness. We prove theoretically that neither metric satisfies marginal propriety. However, Monte Carlo simulations and extensive experiments across diverse right-censored survival modeling scenarios demonstrate that RCLL consistently satisfies this property empirically, while ISBS exhibits only negligible violations under extremely small sample sizes, remaining robust in practice. This reveals a critical dissociation between theoretical impropriety and empirical robustness—a key insight with important implications for metric selection and design. Building on this finding, we propose a new class of loss-function frameworks for survival prediction, grounded in marginal propriety, thereby providing both theoretical guidance and practical foundations for developing future survival scoring rules.

3 citationsRead paper

Minimum L2 and robust Kullback-Leibler estimation

Feb 20, 2026

This study addresses the robustness of parameter estimation under outliers or model misspecification by proposing two novel approaches: minimum weighted L² estimation and robust Kullback–Leibler estimation. These methods achieve robustness through weighted least squares and a robustified empirical Kullback–Leibler divergence, respectively, thereby extending maximum likelihood estimation in a robust manner and establishing theoretical connections to local likelihood principles. The authors derive analytical expressions for the influence function and asymptotic variance, integrating tools from robust statistical inference, influence function analysis, asymptotic efficiency calculations, and semiparametric density estimation. Under normal models, the proposed estimators demonstrate high asymptotic efficiency when the model is correctly specified, while exhibiting superior robustness and statistical performance in the presence of contaminated data.

2 citations1 influentialRead paper
Recent publications

Latest Papers