190 papers
This paper proposes the bivariate rerandomized inverse variance weighted (BRIVW) estimator, a novel method that simultaneously corrects for winner's curse and sample structure in two-sample Mendelian randomization by modeling the joint distribution of genetic associations to provide more accurate and consistent causal effect estimates.
This paper proposes a stable and accurate grid-based method of moments (GRID-MOM) for estimating parameters of the truncated skew-normal distribution by decoupling the shape parameter from location and scale estimates, thereby overcoming numerical instability in existing approaches.
This paper introduces and evaluates two localization strategies for a sequential Markov Chain Monte Carlo data assimilation framework, demonstrating their ability to efficiently handle high-dimensional, nonlinear, and non-Gaussian geophysical models while avoiding weight degeneracy and outperforming traditional ensemble Kalman filters in scenarios with heavy-tailed observation noise.
This paper proposes a computationally efficient and robust method for estimating the location of data on various matrix manifolds by computing the Frobenius median in an ambient Euclidean space and projecting it onto the manifold, while establishing its theoretical properties and demonstrating its effectiveness through simulations and real-world earthquake data.
This paper proposes a latent space Gaussian mixture-of-experts model with a group-fused LASSO penalty to detect abrupt environmental change points in single-cell flow cytometry data of marine phytoplankton, successfully identifying a transition zone between two marine provinces.
This paper proposes a two-stage adaptive design framework for cluster randomised trials that combines test approaches with Pareto optimality to enable early stopping, sample size re-estimation, and trial re-design, thereby addressing high uncertainty in correlation parameters and balancing multi-dimensional cost-efficiency objectives.
This paper proposes a sequential "Clusters-of-Centres" algorithm that utilizes multivariate Cochran-type tests on summary statistics to identify and merge homogeneous groups in multi-centre studies, establishing asymptotic distributions and proving that a multi-round bootstrap variant can recover the true centre partition with high probability.
This paper proposes a new omnibus goodness-of-fit test for univariate continuous distributions that leverages the full covariance structure of trigonometric moments to achieve a well-calibrated asymptotic null distribution, offering a unified, plug-and-play framework with demonstrated accuracy and power across 11 common parametric families.
This book serves as a pedagogical resource that unifies scattered research on conformal prediction and distribution-free inference by curating key results and presenting their proofs in a coherent framework to help readers understand the fundamental technical arguments behind these uncertainty quantification methods.
This paper proposes a hierarchical Bayesian dynamic game framework for competitive inventory and pricing under incomplete information, integrating Bayesian learning, strategic belief updating, and a credible-risk criterion to derive a conservative equilibrium that effectively balances profit maximization with uncertainty management.
This paper establishes a geometric framework using principal fiber bundles to identify fundamental obstructions in learning differential equations from temporal Random Dot Product Graphs, characterizing the interplay between gauge ambiguity, spectral gaps, and holonomy while demonstrating that symmetric dynamics can resolve gauge issues to enable vector field recovery.
This paper proposes a simple algebraic rearrangement of the Yates covariance decomposition for the Brier score that decomposes forecast error into three non-negative terms—variance mismatch, correlation deficit, and calibration-in-the-large—thereby making the conditions for optimal probabilistic forecasting transparent.
This paper establishes new asymptotic properties for the adaptive LASSO estimator in cointegrating regressions with local-to-unity regressors and proposes feasible, uniformly valid confidence regions that overcome the coverage deficiencies and infeasibility of traditional oracle-based methods, as demonstrated through theoretical analysis, simulations, and an empirical application to U.S. unemployment forecasting.
This paper proposes a novel transfer learning framework for survival analysis that enhances prediction accuracy in data-scarce target studies by transferring predictive knowledge from source studies using flexible semiparametric transformation models and an EM algorithm, without requiring access to individual-level source data or assuming shared model parameters.
This paper proposes a semiparametric multivariate functional principal component analysis method for mixed-type mHealth data, which effectively identifies interpretable time-of-day patterns across diverse health domains to stratify mood disorder subtypes.
This paper introduces a unified framework called wcPCA for developing worst-case optimal low-rank approximations across heterogeneous domains, extending the approach to new estimators and matrix completion while proving theoretical guarantees and demonstrating improved robustness in real-world applications with minimal loss to average performance.
This paper introduces the "outrigger" local polynomial estimator, a distributionally adaptive method that achieves minimax optimality across various conditional error distributions without requiring structural assumptions like independence or symmetry, while guaranteeing performance at least as good as standard estimators under Gaussian errors.
This paper proposes a continuous-time stochastic framework for Schnieper's reserving model that utilizes a Poisson measure for claim arrivals and Brownian motion for cost fluctuations, enabling a robust bootstrap method to estimate the full predictive distribution of reserves while naturally ensuring non-negativity and accounting for asymmetry.
This paper proposes a novel Bayesian semi-modular inference framework for copula models that assigns individual influence parameters to each marginal distribution via Bayesian optimization, thereby generalizing existing two-module approaches to robustly handle varying levels of marginal misspecification while efficiently relaxing the discrete search over cut configurations.
This paper proposes a doubly robust estimator combined with a novel jackknife spatial HAC variance correction to achieve valid asymptotic inference when using machine learning predictions for missing-at-random labels in the presence of spatial dependence, addressing the instability caused by cross-fitting-induced correlations.