177 papers
This paper proposes a framework and develops several estimators with varying degrees of robustness for estimating and conducting inference on heterogeneous principal causal effects under principal ignorability, demonstrating their theoretical properties and practical application through the Camden Coalition hotspotting randomized trial.
This paper proposes an efficient semiparametric estimation method using genetic instrumental variables to address sampling uncertainty in the marginal treatment effects framework, revealing that individuals most prone to excessive alcohol consumption suffer the largest adverse effects on blood pressure.
This paper introduces Adaptive Importance Sampling and Stratified Subsampling estimators that achieve minimax-optimal rates for robust high-dimensional sparse regression under heavy-tailed noise, contamination, and temporal dependence, while also providing fully specified de-biasing procedures for valid confidence intervals and demonstrating superior empirical performance over uniform subsampling.
This paper proposes a novel integrated online reliability prediction framework for satellite electronics that combines a Wiener process-based degradation model with a two-stage adaptive active learning strategy to significantly improve prediction accuracy while reducing data requirements under limited and variable operational conditions.
This paper proposes a novel hierarchical multi-task multi-fidelity (H-MT-MF) framework for Gaussian process-based surrogate modeling that unifies inter-task information sharing and fidelity-dependent uncertainty handling to significantly improve prediction accuracy and data efficiency in manufacturing systems with heterogeneous data sources.
This paper presents a unified framework for traditional and convex Non-negative Matrix Factorization (NMF) under Negative Binomial and Tweedie distributions, deriving novel multiplicative update rules via Majorize-Minimization and demonstrating through empirical evaluation that appropriate noise model selection and convex formulations significantly improve feature recovery in overdispersed data.
This paper introduces Bayesian Generative Modeling (BGM), a unified framework that leverages a stochastic iterative Bayesian updating algorithm to learn a single generative model capable of performing arbitrary conditional inference with principled uncertainty quantification, without requiring retraining for different conditioning structures.
This paper critiques the prevalent reliance on fixed-threshold metrics in machine learning evaluation by advocating for a consequentialist framework that prioritizes proper scoring rules like the Brier score, supported by a new decision-theoretic mapping, a practical Python package called `briertools`, and a clipped Brier score variant to bridge the gap between theoretical utility and current practices.
This paper introduces DR-FoS, a novel doubly-robust estimator for the Functional Average Treatment Effect (FATE) that ensures consistent estimation and valid simultaneous inference even when either the outcome or treatment assignment model is misspecified, demonstrating its effectiveness through simulations and a real-world application to the SHARE dataset.
This paper introduces efficient, parameter-free geodesic slice sampling algorithms for generating samples from probability distributions on the sphere, proving their uniform ergodicity and demonstrating superior performance over standard methods like random-walk Metropolis-Hastings and Hamiltonian Monte Carlo in challenging directional data scenarios.
This paper formalizes the order-dependence of the moving-range sigma estimator by decomposing its total variance into order-invariant and adjacency-specific components via random permutation, revealing that under normal sampling, the adjacency effect accounts for the majority of its efficiency loss relative to the standard deviation estimator.
This paper introduces Sigmoid-FTRL, an adaptive experimental design that overcomes the non-convexity challenges of AIPW estimators to achieve the minimax optimal Neyman Regret rate of while providing valid asymptotic inference.
This paper introduces the Poisson tensor completion (PTC) estimator, which leverages inter-sample relationships and models histogram bins as a non-homogeneous Poisson process to achieve a low-rank, non-negative tensor decomposition that significantly outperforms standard histogram-based estimators for sub-Gaussian distributions.
This paper introduces an efficient algorithm that rapidly computes a complete curve of post hoc false discovery proportion bounds along a path of increasing selection sets by leveraging the forest structure of the reference family and the incremental nature of adding single hypotheses.
This paper demonstrates that by strategically combining sample splitting with specific nuisance function tuning strategies (such as undersmoothing or oversmoothing), both plug-in and first-order bias-corrected estimators can achieve minimax rates of convergence for doubly robust functionals across all Hölder smoothness classes, overcoming limitations of existing literature.
This paper proposes TV-Select, a unified framework that simultaneously identifies relevant variables and distinguishes between constant and time-varying effects in longitudinal models by employing a doubly penalized B-spline approach with group Lasso and roughness penalties to achieve accurate structural recovery, smooth estimation, and improved predictive performance.
This paper proposes a transparent, model-based approach to isolate and decompose the energy consumption effects of intelligent district heating control algorithms from other building changes, addressing the limitations of existing evaluation methods using a decade of real-world data.
This paper proposes a nonparametric density estimation method for compositional data with missing values using an adaptive Dirichlet kernel and inverse probability weighting, demonstrating its superior finite-sample performance over log-ratio transformation approaches and its practical utility in analyzing leukocyte composition data.
This paper proposes a unified calibration framework that integrates heterogeneous internal and auxiliary information into randomized experiments under covariate-adaptive randomization via convex optimization, ensuring asymptotic validity and a no-harm efficiency guarantee while accommodating scenarios with growing numbers of strata and information sources.
This paper introduces a novel Fréchet regression framework for multivariate distributional responses that leverages the nonparanormal transport metric to efficiently decompose the problem into marginal and dependence regressions, offering theoretical guarantees on convergence and dimensionality while demonstrating practical utility in continuous glucose monitoring.