200 papers
This paper explores the strategic setup, remit, and critical success factors of dedicated statistical methodology groups within the pharmaceutical industry, emphasizing their role in driving innovation, improving clinical trial efficiency, and maximizing the probability of drug development success through rigorous validation and cross-functional collaboration.
This paper introduces diagnostic transport maps as a method to recalibrate potentially misspecified predictive distributions for high-dimensional inputs, enabling real-time local diagnostics and improved accuracy for rare events, as demonstrated in tropical cyclone intensity forecasting.
This paper proposes and analyzes non-penalized and sparse-penalized deep neural network estimators based on the minimum error entropy principle for nonparametric regression with strongly mixing dependent observations, establishing that they achieve minimax optimal convergence rates up to a logarithmic factor.
This paper extends conformal e-prediction to scenarios involving observed confounding between the random object and its label, addressing both independent and identically distributed (IID) data and cases permitting dependence between observations.
This paper introduces a three-dimensional height-augmented ReLU network architecture that achieves significantly more efficient exponential approximation rates for analytic functions and provides the first quantitative, non-asymptotic high-order approximation for general functions, thereby advancing the theoretical foundation for designing parameter-efficient neural networks.
Co-Diffusion is a novel two-stage latent diffusion framework that enhances generalizable drug-target affinity prediction by aligning embeddings in an affinity-steered manifold and employing modality-specific diffusion as a regularizer, thereby achieving superior zero-shot performance on unseen molecular scaffolds and protein families compared to state-of-the-art methods.
This paper introduces a partition-based functional ridge regression framework that decomposes coefficient functions into dominant and weaker components to apply differential penalization, thereby improving numerical stability, interpretability, and predictive performance in high-dimensional functional linear models without relying on explicit variable selection.
This paper argues that standard stateful pseudorandom number generators undermine the causal validity of agent-based models by linking random draws to execution order rather than specific events, and proposes using event-keyed hashing with counter-based generators to restore causally coherent paired counterfactual comparisons.
This paper proposes a novel, computationally efficient estimator for polychoric correlation that minimizes a robust loss function to withstand partial model misspecification—such as careless responding—without requiring assumptions about the nature or extent of the errors, thereby offering a more reliable alternative to traditional maximum likelihood estimation in rating data analysis.
This paper establishes a central limit theorem for large network formation models with strategic interactions and homophily by adapting stabilization conditions from geometric graphs and deriving interpretable primitive conditions based on branching process theory to justify practical inference procedures.
The paper introduces BLAST, a Bayesian transfer learning framework for high-dimensional linear regression that utilizes adaptive shrinkage and source selection to improve predictive accuracy, uncertainty quantification, and inference by effectively balancing information sharing across multiple data sources while mitigating negative transfer.
This paper proposes a principled Bayesian response-adaptive randomization method that stabilizes the high variability of Thompson sampling by introducing a null hypothesis of equal treatment effectiveness, thereby balancing patient allocation to superior treatments with statistical reliability and inferential validity.
This paper extends the wgsLR model for forensic shotgun DNA sequencing to accommodate asymmetric and unknown genotyping error probabilities between trace and reference samples, demonstrating the model's robustness and providing conservative strategies for calculating weight of evidence, all implemented in an updated R package.
Motivated by robotic swarm monitoring, this paper proposes the Multi-rank Subspace-CUSUM (MRS-C) procedure for detecting low-rank changes in high-dimensional streaming data, proving its first-order asymptotic optimality and demonstrating its effectiveness through theoretical analysis and real-world applications.
This paper demonstrates that a noncentral Wishart mixture of noncentral Wishart distributions with identical degrees of freedom remains a noncentral Wishart distribution, a result used to derive the finite-sample distribution for testing random effects in two-factor and general factorial design models with multivariate normal data.
This paper proposes a stable survival extrapolation framework that integrates Bayesian mortality models with flexible parametric polyhazard models to leverage external registry data as an anchor, thereby improving the robustness and interpretability of mean survival estimates in complex clinical scenarios such as breast cancer, melanoma, and cardiac arrhythmia.
This paper introduces a novel nonparametric framework for detecting changepoints in the mean direction of toroidal and spherical meteorological data by leveraging intrinsic geometry to define a curved dispersion matrix and Mahalanobis distance, establishing the statistical properties of the proposed tests and validating them on wind-wave directions and cyclonic storm paths.
This paper introduces an exploratory restricted latent class model that accommodates polytomous responses, ordinal multi-attribute states with correlated attributes via a multivariate probit specification, and respondent-level covariates, demonstrating its effectiveness in recovering parameters and revealing complex latent structures in depression diagnosis beyond traditional single-factor approaches.
This paper proposes a new Metropolis-Hastings algorithm that leverages control variates and subsampling to achieve exact, efficient Bayesian inference on large datasets while satisfying detailed balance and outperforming existing methods in computational efficiency and required subsample size.
This paper introduces a method for identifying causal generalized linear models by leveraging Pearson risk invariance and maximum expected likelihood, enabling causal discovery from a single data environment without requiring multiple heterogeneous settings.