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Improved Baselines with Representation Autoencoders Calibeating for general proper losses: A Bregman divergence approach Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles Reasoning Models Don't Just Think Longer, They Move Differently TabPFN-3: Technical Report Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models Towards a holistic understanding of Selection Bias for Causal Effect Identification Adaptive Kernel Density Estimation with Pre-training Coreset-Induced Conditional Velocity Flow Matching RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage Model-based Bootstrap of Controlled Markov Chains Online Learning-to-Defer with Varying Experts Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions One-Step Generative Modeling via Wasserstein Gradient Flows Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty A Composite Activation Function for Learning Stable Binary Representations Adaptive Calibration in Non-Stationary Environments Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage On Variance Reduction in Learning Mean Flows When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? 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On the Impact of Insurance on Households Susceptible to Random Proportional Losses: An Analysis of Poverty Trapping
Kira Henshaw, Jorge Ramirez, José Miguel Flores-Contró, Enrique · 2023-09-22 · via stat updates on arXiv.org

The trapping probability, $ψ$, as defined in Kovacevic and Pflug (2011), is modelled by assuming proportional capital losses, both in the case where there is no insurance and in the case where insurance is purchased by the household. Insurance coverage is likewise proportional, mirroring the structure of quota-share contracts, which are both prevalent in practice and analytically convenient. New closed formulae for $ψ$ are obtained in the case of no insurance when the distribution of the remaining proportion of capital is a power law, extending the results in Kovacevic and Pflug (2011). When proportional insurance is acquired and the remaining proportion of capital is uniformly distributed on $[0,1]$, $ψ$ satisfies a non-local differential equation whose analysis is based on the properties of diffusion processes. The non-local nature of the equation can be addressed using iterative solution methods, leading to a constructive determination of the trapping probability. Constraints on the parameters governing the capital process are derived in both the uninsured and insured cases to prevent the certainty of trapping. Numerical calculations are used to determine the trapping probability for the insured process and to illustrate the impact of different parameters. Consequences on the trapping probability for vulnerable non-poor populations with initial capital slightly above the poverty line are discussed.