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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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6 results for “underreporting”

Optimal Underreporting and Competitive Equilibriumq-fin.MF

Paper analyzes strategic underreporting in competitive insurance markets.

problem Strategic underreporting by insureds in competitive insurance markets.

method Develops a dynamic insurance market model with two competing companies and a continuum of insureds, examines the interaction between strategic underreporting and competitive pricing under a Bonus-Malus System framework.

result Establishes the existence and uniqueness of the insureds' optimal reporting barrier and its dependence on BMS premiums; proves the existence of Nash equilibrium premium strategies.

insurance markets strategic underreporting competitive pricing Bonus-Malus System Nash equilibrium

2026-01-19abs ↗pdf ↗

Learning Models from Data with Measurement Error: Tackling Underreportingstat.ML

Measurement error in observational datasets can lead to systematic bias in inferences based on these datasets. As studies based on observational data are increasingly used to inform decisions with real-world impact, it is critical that we develop a robust set of techniques for analyzing and adjusting for these biases. …

2019-01-25abs ↗pdf ↗

Domain Adaptation under Missingness Shiftcs.LG

Paper tackles missing data shifts in domains, showing how to adapt models.

problem Missing data mechanisms differ between source and target domains.

method Introduced DAMS, showing covariate shift under certain conditions, and providing adjustments for missingness.

result Simple adjustments can improve target model performance even when missingness rates vary.

domain adaptation missing data covariate shift underreporting

2022-11-03abs ↗pdf ↗

Realistic Evaluation of Deep Semi-Supervised Learning Algorithmscs.LG

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled data when labels are limited or expensive to obtain. SSL algorithms based on deep neural networks have recently proven successful on standard benchmark tasks. However, we argue that these benchmarks fail to address many issues that th…

2018-04-24abs ↗pdf ↗

A data-driven merit order: Learning a fundamental electricity price modelstat.AP

A new data-driven model forecasts electricity prices efficiently.

problem Forecasting electricity prices using traditional methods.

method Integrates data-driven and fundamental models, learns from historical data.

result Significantly improves forecasting accuracy compared to existing models.

electricity pricing data-driven models merit order forecasting machine learning

2025-01-06abs ↗pdf ↗

Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Braziq-bio.PE

This study analyzes the effects of lifting lockdowns on Brazil's COVID-19 spread.

problem Understanding the impact of lifting lockdowns on Brazil's COVID-19 spread.

method Extended SEIR model with on/off strategy and developed a neural regressor.

result Lifting lockdowns could lead to a more severe second wave of COVID-19.

COVID-19 Brazil lockdown SEIR model neural regressor

2020-04-15abs ↗pdf ↗