144 papers
This paper proposes a novel data-driven framework using offline reinforcement learning and survival analysis to estimate optimal pricing and inventory control policies in sequential environments with censored and dependent demand, overcoming challenges like missing profit information and non-stationarity by approximating the problem as a high-order Markov decision process.
This paper advances the theory of small area prediction intervals by analytically demonstrating that the coverage error of empirical best linear predictors depends critically on the existence of a pivot, revealing that standard parametric bootstrap methods fail to achieve optimal accuracy without it and proposing a double parametric bootstrap approach to correct this deficiency.
This paper establishes the theoretical foundations of Vecchia approximations for Matérn Gaussian Processes by characterizing their conditional distributions via polynomial interpolation, deriving key probabilistic properties, and proving that the resulting posterior achieves optimal minimax convergence rates in nonparametric regression.
This paper proposes two robust criteria and a corresponding sampling-based difference-of-convex algorithm for learning optimal treatment rules that maximize truncated mean survival time and buffered survival probabilities in the presence of censored survival data, demonstrating superior performance through simulations and an AIDS clinical trial application.
This paper establishes asymptotic properties of Bayesian cut distributions, including a Bernstein-von Mises theorem and Laplace approximation, and proposes a Posterior Bootstrap algorithm to achieve nominal frequentist coverage in the presence of model misspecification.
This paper introduces Scorio, an open-source library that formalizes and implements statistical methods for reliably ranking reasoning LLMs under test-time scaling, demonstrating that most approaches achieve high agreement with a Bayesian gold standard across multiple Olympiad-style math benchmarks.
This paper proposes a method for estimating failure rates in stationary periodic Rate Monotonic real-time systems by approximating response time distributions with an inverse Gaussian mixture model, whose parameters are derived using a re-parameterized Expectation-Maximization algorithm and validated through extensive simulations.
This paper proposes an unsupervised hyperspectral image clustering method that improves upon existing Wasserstein space dictionary learning by utilizing unbalanced Wasserstein barycenters to learn a robust lower-dimensional representation, thereby mitigating issues of class blurring and sensitivity to outliers and noise.
This paper investigates the construction of positive definite thresholding functions for correlation matrices, establishing that preserving positive semidefiniteness through soft-thresholding inevitably induces a geometric collapse in the feature space, thereby limiting the recoverable signal.
This paper proposes "ForwardFlow," a frequentist deep learning framework that utilizes a branched neural network trained on simulated data to directly estimate statistical parameters, demonstrating advantages such as finite sample exactness, robustness to contamination, and the ability to automatically approximate complex algorithms like the EM-algorithm.
This paper establishes a strong Gaussian approximation for high-dimensional non-degenerate U-statistics with diverging dimensions under mild assumptions, utilizing a sharp martingale maximal inequality to provide a unified framework for functional limits and inference without relying on -type bounds or bootstrap arguments.
This paper proposes a surrogate-assisted targeted minimum loss estimator that achieves asymptotic linearity and double robustness for causal inference with delayed outcomes under administrative censoring by leveraging a surrogate-bridge representation to avoid unstable inverse-probability weighting and eliminate second-order bias without directly estimating the conditional surrogate law.
This paper demonstrates that the standard criterion for identifying minimal sufficient statistics is flawed due to its dependence on specific versions of Radon-Nikodym derivatives, and subsequently proposes a version-robust alternative that extends existing methods to broader analytic and standard Borel spaces while also critiquing a separate criterion by Pfanzagl.
This paper establishes that a continuous-time diffusion approximation of policy gradient for stochastic bandits achieves logarithmic regret with a learning rate of , while demonstrating that a significantly smaller learning rate of is necessary to avoid linear regret in specific instances.
This paper proposes a novel operator-level ARCH framework for data in general separable Hilbert spaces that models the full evolution of the conditional covariance operator, establishing conditions for stationarity and deriving consistent estimators, which are validated through simulations and an application to high-frequency intraday returns.
This paper establishes the first universality-breaking frontier in shuffle privacy by characterizing the asymptotic behavior of concentrated local randomizers that fail classical Gaussian limits, proving convergence to explicit Poisson, Skellam, and compound-Poisson shift experiments and providing a complete three-regime picture of shuffle privacy limits.
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 paper establishes that for discretely observed oscillating Brownian motion, the maximum likelihood estimator of the self-organized criticality level achieves -consistency and converges stably to a bivariate Poisson-type distribution, despite the non-standard challenge posed by the discontinuity of the transition density at the true parameter.
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.