Probability & Statistics · Mathematics & Statistics
Methodology
The newest papers in Methodology that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
353 papers, from 4 September 2026 to 5 November 2026: 353 arXiv preprints.
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Newest papers, page 14
High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series
Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied.Convergence-Aware Pareto Selection of Covariate Scaling Transformations for Markov Deterioration Hazard Models: Evidence from Bridge Inspection Data
Maximum likelihood estimation of Markov deterioration hazard models for infrastructure asset management is sensitive to the numerical conditioning of explanatory covariates, yet production pipelines…Change detection with conformal martingales: new optimal constructions, and suboptimality of existing methods
We study distribution-free sequential changepoint detection for independent observations with unknown and unrestricted pre- and post-change laws.Functional Causal Discovery via Conditional Covariance Ordering
We study causal discovery where each node is a random function.Beyond the Illusion of Power: Calibrating Quasi-Experiments in Observational IS
Information systems (IS) researchers increasingly use quasi-experimental methods such as difference-in-differences (DiD) and instrumental variables (IV) to recover causal effects from observational…Model-agnostic noise reduction for high-dimensional time series data
We develop a model-agnostic framework for noise reduction in high-dimensional time series that explicitly targets optimal recovery of a low-dimensional latent dynamic component contaminated by…Improving Ensemble Filters with Flow Matching
Data assimilation estimates a dynamical state from partial and noisy observations.Beyond Scalar Sensitivity: Activation-Aware Mixed-Precision LLM Quantization with Cross-Layer Refinement
Mixed-precision weight quantization is commonly formulated as a Multiple-Choice Knapsack Problem (MCKP), yet existing solvers rely on scalar sensitivity proxies that collapse each weight matrix's…Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target
Forecasters often score the same units per date against one standardized realized outcome.Optimal Sequential Annotations for Off-Policy Evaluation
Offline reinforcement learning and off-policy evaluation evaluates dynamic treatment rules based on retrospectively collected data prior to deployment.Automatic depth-based local center clustering via $β$-integrated local depth and adaptive grouping
Clustering is an unsupervised learning technique that partitions unlabeled data into groups.Additive Nonparametric Regression with Spatial and Network Objects
This article is motivated by an imaging application from the Adolescent Brain Cognitive Development (ABCD) study, aiming to predict task-based brain activation maps from t-fMRI using cortical…Rolling Conformal Prediction in Sequential Model Training
We introduce Rolling Conformal Prediction (rolling-CP), a distribution-free predictive inference method for the setting of sequential model training.CVaR anchor regression protects against rare shifts
We study prediction in new environments when training data contain rare, large shifts.Doubly robust target inference for generalized linear regression with completely missing covariates
Large-scale multipurpose cohort studies and biobanks often omit covariates needed for specific downstream analyses.Model-Agnostic Feature Selection via LOCO-Guided Adaptive Minipatch Sampling
Black-box machine learning models increasingly deliver strong predictions, but extracting useful information from them, such as a set of important features, remains challenging.A Three-Way Testing Framework for Quantifying Epistemic Calibration Uncertainty in SBI
Current experimental scientists increasingly rely on simulation-based inference (SBI) to invert complex models with intractable likelihoods.Tensor Completion using Subspace Information
Tensor completion has attracted significant attention in both applications and theoretical research.Selective Inference for CART with Binary Outcomes
Binary classification trees select subgroups using the same outcomes later used to assess their differences.Calibration Count Reuse: Validity Does Not Determine Efficiency
Calibration count reuse raises separate validity and efficiency questions.