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Weak convergence of Bayes estimators under general loss functions
Robin Requadt, Housen Li, Axel Munk
https://arxiv.org/abs/2510.05645 https://arxiv.org/pd…

Weak convergence of Bayes estimators under general loss functions
We investigate the asymptotic behavior of parametric Bayes estimators under a broad class of loss functions that extend beyond the classical translation-invariant setting. To this end, we develop a unified theoretical framework for loss functions exhibiting locally polynomial structure. This general theory encompasses important examples such as the squared Wasserstein distance, the Sinkhorn divergence and Stein discrepancies, which have gained prominence in modern statistical inference and mach…

On data-driven robust distortion risk measures for non-negative risks with partial information
Xiangyu Han, Yijun Hu, Ran Wang, Linxiao Wei
https://arxiv.org/abs/2508.10682 http…

On data-driven robust distortion risk measures for non-negative risks with partial information
In this paper, by proposing two new kinds of distributional uncertainty sets, we explore robustness of distortion risk measures against distributional uncertainty. To be precise, we first consider a distributional uncertainty set which is characterized solely by a ball determined by general Wasserstein distance centered at certain empirical distribution function, and then further consider additional constraints of known first moment and any other higher moment of the underlying loss distributio…

Ordinal Label-Distribution Learning with Constrained Asymmetric Priors for Imbalanced Retinal Grading
Nagur Shareef Shaik, Teja Krishna Cherukuri, Adnan Masood, Ehsan Adeli, Dong Hye Ye
https://arxiv.org/abs/2509.26146

Ordinal Label-Distribution Learning with Constrained Asymmetric Priors for Imbalanced Retinal Grading
Diabetic retinopathy grading is inherently ordinal and long-tailed, with minority stages being scarce, heterogeneous, and clinically critical to detect accurately. Conventional methods often rely on isotropic Gaussian priors and symmetric loss functions, misaligning latent representations with the task's asymmetric nature. We propose the Constrained Asymmetric Prior Wasserstein Autoencoder (CAP-WAE), a novel framework that addresses these challenges through three key innovations. Our approach e…

Quantile Function-Based Models for Neuroimaging Classification Using Wasserstein Regression
Jie Li, Gary Green, Jian Zhang
https://arxiv.org/abs/2508.21523 https://

Quantile Function-Based Models for Neuroimaging Classification Using Wasserstein Regression
We propose a novel quantile function-based approach for neuroimaging classification using Wasserstein-Fréchet regression, specifically applied to the detection of mild traumatic brain injury (mTBI) based on the MEG and MRI data. Conventional neuroimaging classification methods for mTBI detection typically extract summary statistics from brain signals across the different epochs, which may result in the loss of important distributional information, such as variance, skewness, kurtosis, etc. Our…