arXiv preprints from January 1, 2026 through September 23, 2026 — 06:08:27 EST
We study an Estimated Time of Arrival (ETA)-based traffic-coordination framework for Urban Air Mobility corridors with merging at constrained waypoints (CWPs), where approved ETAs at CWPs serve as Required Times of Arrival (RTAs). Vehicle operators submit ETA plans at the merging point for approval by corridor-management authorities...
In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data...
Under the ICH E9 (R1) addendum, treatment policy strategies for intercurrent events target the treatment effect regardless of treatment discontinuation. Sequential multiple imputation (MI) models that condition each visit's imputation on discontinuation status or pattern reduce bias relative to mixed models and standard MI, but...
Instance-wise feature selection is a valuable tool for interpreting labeled data and the predictions of black-box models. In contrast to global feature selection techniques, instance-wise methods dynamically identify important features for each instance. A growing number of methods learn a selector, which identifies important...
Bayesian dynamic borrowing (BDB) methods leverage historical data to reduce treatment effect uncertainty, yet existing approaches rely on parametric outcome models susceptible to misspecification. We propose the nonparametric latent exchangeability prior (NP-LEAP), an outcome-agnostic, assumption-lean framework to borrow information...
Generalized Bayesian inference uses weights of the form $\exp\{-β_n\ell_{n}(θ)\}$, even when the loss is available only through simulation, numerical integration, or subsampling. Exponentiating an unbiased loss estimate changes the target, and when $β_n\asymp n$ an ordinary Monte Carlo (MC) loss estimate with $M^{-1}$ variance needs a...
Seasonal infectious-disease interventions are commonly evaluated with interrupted time-series or pre--post designs that align epidemics by calendar week. When epidemic onset, speed or peak timing differs between seasons, such comparisons confound a shift in epidemic phase with a change in disease burden. We propose a Bayesian causal...
The log-rank test and Kaplan--Meier plot are standard tools for analyzing time-to-event data in randomized clinical trials, yet neither provides a summary of the magnitude of the treatment effect. Practitioners typically fill this gap by reporting a hazard ratio from a Cox proportional-hazards model or an acceleration factor from an...
Dataset alignment is a central step in data analysis across science and engineering, where the goal is to match observations between datasets. Entropic Optimal Transport (EOT) offers a computationally tractable framework for this task by encoding cross-dataset affinities in a transport plan. However, when two datasets are sampled from...
This paper studies the regret analysis for parallel Gaussian process (GP) bandit optimization. The known regret upper bounds for the widely used GP batched upper confidence bound and GP batched Thompson sampling (GP-BTS) suffer from a multiplicative factor with respect to the batch size $Q$. To avoid this degradation, existing...
Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an experiment. However, this optimization is computationally challenging for common utilities and complex models. This is especially so for sequential or adaptive designs, where design and data...
Problem-solving log process data from computer-based assessments provide detailed information about how respondents approach and complete tasks. However, the resulting action sequences are complex and noisy, making it difficult to identify specific behaviors associated with successful performance. This paper proposes a...
Statistical inverse problems have garnered significant attention in recent years due to the growing importance of statistical learning theory and functional analytic approaches in the fields of machine learning and artificial intelligence. In this paper, we investigate the stable approximation of the element $u^{\dagger}$ that...
Ornstein-Uhlenbeck processes with flexible multivariate drift matrices are powerful models for capturing coupled, asymmetric, and damped-oscillatory mean reversion. However, likelihood-based inference is challenging because the drift matrix must remain Hurwitz stable, while likelihood and gradient evaluations require repeated, costly...
Interference occurs when one individual's treatment or exposure affects another individual's outcome. In particular, we assume partial interference, where individuals are divided into groups such that there is no interference between individuals in different groups. In observational studies, inverse probability weighting (IPW) based...
The combination of traditional statistical models and neural network (NN) components into semi-structured hybrid models is an intriguing approach to construct models that, ideally, combine traditional interpretability with the unprecedented flexibility of NNs. In order to preserve interpretability, it is usually necessary to restrict...
In multi-institutional studies, different parties hold distinct feature blocks for partially overlapping sets of individuals. Responses may also be missing for some records. In such settings, we propose Assisted Learning with Block-Missing Data (ALB) for sparse high-dimensional linear estimation and coordinatewise inference without...
LLM judges are often evaluated with a single prompt and only a few repeated calls. When their verdicts vary, it remains unclear whether the variation comes from sampling noise within a prompt or systematic differences across prompts. We formalize this distinction using a second-order response law: the distribution of...
Aggregate accuracy hides where models succeed and fail. Estimating conditional performance profiles from gold labels alone is expensive, while cheap auxiliary signals such as LLM-judge scores, pairwise comparisons, confidence scores, and judge-disagreement features can be collected for every benchmark item but are often biased or...
We propose a regression model for the extreme tail of a response variable, in which covariates rescale the tail without changing its shape. A single covariate-dependent function then characterizes the entire conditional tail, in contrast to extreme quantile regression, which targets a quantile at a pre-specified level. The tail shape...
Identification of dominant polynomial-chaos modes is usually formulated as a sparse-regression problem on a sampled multivariate polynomial dictionary. We develop coded Hankel polynomial chaos (CH-PC), a complementary spectral formulation for dominant-mode identification. A finite generating transform converts PCE coefficients into a...
Coresets distill large datasets into small, representative subsets for efficient downstream learning. Yet Optimal Transport (OT)-based selection typically requires intensive computation of transport plans, limiting scalability. We introduce a scalable Sinkhorn coreset method that permits closed-form updates of the entropically...
Group Independent Component Analysis (gICA) is widely used to decompose high-dimensional functional MRI data into interpretable brain networks. However, conventional gICA primarily identifies components shared across subjects. This group-level assumption can limit the recovery of networks present only in individuals or subject...
Estimating high dimensional Generalized Structural Equation Models presents severe computational challenges. Traditional simultaneous estimators frequently suffer from numerical instability and prohibitive computational costs. Moreover, there are no tractable algorithms for families such as Poisson, negative binomial, and gamma. To...
Personalized predictive modelling has been growing rapidly with the increasing availability of Electronic Health Records. This approach aims to improve a model's predictive performance by fitting a unique model to each individual. We train the model on a subset of the training data consisting of individuals similar to the individual...