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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 Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks Optimal Experiments for Partial Causal Effect Identification Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors GRALIS: A Unified Canonical Framework for Linear Attribution Methods via Riesz Representation Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Perturbation is All You Need for Extrapolating Language Models Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization Realizable Bayes-Consistency for General Metric Losses Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Segmenting Human-LLM Co-authored Text via Change Point Detection Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching The Geometric Mechanics of Contrastive Representation Learning: Alignment Potentials, Entropic Dispersion, and Cross-modal Divergence Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models First-Order Efficiency for Probabilistic Value Estimation via A Statistical Viewpoint Robust and Fast Training via Per-Sample Clipping
Testing Preferential Sampling
[Submitted on 12 Jun 2026] · 2026-06-15 · via stat updates on arXiv.org

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Abstract:Geostatistics aims to infer a spatially continuous phenomenon from observations collected at a finite number of locations, frequently measured with error. Whenever there is stochastic dependence between the spatial and sampling processes, preferential sampling occurs. Ignoring this problem drives to incorrect and biased estimates and, therefore, recognizing it is quite important, but not always simple to execute and understand. In this work, a test for assessing preferential sampling, simple and easy to implement, is presented, overcoming the previous concerns. It is based on the dependence between the number of sampled points and the values of the corresponding measures. The performance of the proposed test id assessed through a large simulation study, which consideres different levels of preferentiability, relation with a covariate, different sample sizes and different test procedure conditions. The results are quite encouraging, with high levels of correct preferential sampling detections, further confirmed by the test application to already known real data sets of lead concentrations in moss samples and red and blue shrimp capture data.

Submission history

From: Isabel Natário [view email]
[v1] Fri, 12 Jun 2026 16:40:01 UTC (273 KB)