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 18
Filtering without recursion and some of its uses in financial economics
We develop a filter for time series, defined at each time $t$ as the minimizer of a discounted convex combination of observed and expected losses.Adaptive Conformal Inference Under Delayed Feedback: Coverage Guarantees and a Delay-to-Memory Diagnostic
Adaptive Conformal Inference (ACI) extends conformal prediction to non-exchangeable settings by adjusting the nominal miscoverage level online in response to recent coverage errors.Distributed Lag Neural Additive Models
We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonlinear effects distributed over lags.The Role of Uncertainty in Assessing the Fairness of Machine Learning Models
Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure.Bayesian Matrix-Valued Graphs for Context-Dependent Multivariate Relationships
Many scientific graphs attach several variables to each node, so a single scalar edge weight cannot describe direction-dependent interactions.Cluster-Based Dimensionality Reduction by Nonparametric Distributional Screening
We consider dimensionality reduction for high-dimensional observations accompanied by a supplied partition into two or more clusters.A Subsampled Davis-Kahan Bound for Large-Scale Eigenspace Estimation
The Davis-Kahan theorem is a fundamental tool in spectral analysis, providing quantitative control over the distance between the eigenspaces of a symmetric matrix and its perturbation.Diagonal Attenuation: A Finite-Sample Correction for PCA
Principal component analysis (PCA) can rotate away from its population target when a covariance matrix is estimated from limited data.Causal DAG Identification for Count Data via Poisson Thinning Structural Equation Models
Count-valued variables arise in many scientific and applied settings, yet explicit structural models that allow full identification of causal DAGs from observational data remain limited.Robust conditional dimension reduction for dissimilarity data
Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed covariates that represent known sources of variation in the data.Bias-Corrected Subspace Intersection: Minimax-Optimal Shared Subspace Estimation in Multi-View Data
Estimating a low-dimensional subspace shared across noisy data matrices is a fundamental problem in multi-view matrix estimation.Nonparametric Hypothesis Testing of High-dimensional Clustering With Application to Single-cell RNA Data
Single-cell RNA sequencing studies routinely use clustering to define putative cell types and cell states, yet the observed separation may arise from sampling variability rather than genuine…A complete characterization of sequential testability and change detectability in i.i.d. models
We give a necessary and sufficient condition for the existence of power-one sequential tests in an i.i.d. composite testing problem.