183 papers
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 extends Causal Normal Form Games to sequential settings by introducing Sequential Causal Multi-Agent Systems, but its comprehensive theoretical and empirical analysis reveals that, under standard rational assumptions, these causal frameworks offer no welfare advantage over classical Stackelberg equilibrium, thereby highlighting a fundamental incompatibility between rational choice and causal reasoning benefits in current game-theoretic models.
This paper proposes the Time-series Neural Causal Model VAE (TNCM-VAE), a novel framework that integrates variational autoencoders with structural causal models to generate synthetic financial time series that preserve both temporal dependencies and causal relationships, thereby enabling robust counterfactual analysis and risk assessment.
This paper introduces Augmented Time Series Causal Models (ATSCM), a novel framework that integrates neural causal discovery with counterfactual reasoning to dynamically model complex, time-varying causal relationships in energy markets and enable interpretable scenario analysis for electricity price formation.
This paper proposes a novel summary-statistics-based joint analysis method that controls the joint local false discovery rate (Jlfdr), demonstrating through simulations and empirical data that it offers superior power over traditional meta-analysis methods, particularly when analyzing heterogeneous genome-wide association study datasets.
This paper proposes two probabilistic measures, Reproducibility Rate (RR) and False Irreproducibility Rate (FIR), to quantitatively evaluate the behavior of primary positive associations in replication studies, offering tools to guide study design and identify potentially true associations among irreproducible findings.
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 a Bayesian Synthetic Likelihood (BSL) framework integrated with Hamiltonian Monte Carlo to enhance Multilevel Network Meta-Regression by leveraging available subgroup-level summary statistics to impute missing individual-level covariates, thereby improving population-adjusted treatment comparisons in the presence of incomplete data.
This paper introduces a novel framework combining the expected Goal Action (xGA) metric with a Player's Restricted Shapley (PRS) statistic to quantify individual player contributions in football shot actions by modeling team interactions through tactically admissible passing coalitions, as demonstrated in an analysis of the 2022/23 Serie A season.
This paper presents a unified Bayesian optimization framework using Gaussian processes with derivative observations and advanced extensions like Optimal Transport and random Fourier features to efficiently accelerate the search for minima and saddle points on potential energy surfaces, bridging theoretical formulation with practical implementation through accompanying Rust code.
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 develops a causal survival framework for platform trials that demonstrates while pooling non-concurrent controls can improve precision under strict assumptions, the most robust approach to avoid bias and maintain efficiency is to target concurrent causal estimands using covariate-adjusted doubly robust estimators that rely solely on concurrent controls.
This paper reinterprets partial copulas as a nonlinear analogue of partial correlation, establishes theoretical bounds linking conditional dependence properties to the partial copula's form, and demonstrates through simulations their utility in describing covariate-adjusted dependence for causal inference applications.
This paper proposes a fully nonparametric method for constructing tolerance intervals using a calibrated Gibbs posterior based on asymmetric Laplace loss, which ensures reliable frequentist coverage and often yields shorter intervals than traditional benchmarks across diverse applications.
This paper proposes a heterogeneity-adaptive meta-analysis framework for linear models that utilizes a Kullback-Leibler divergence penalty to shrink dataset-specific distributions toward a centroid, thereby achieving superior estimation efficiency and valid inference without assuming parameter homogeneity across studies.
This paper demonstrates that under the ICH E9(R1) framework, the choice of a non-inferiority margin must be explicitly aligned with the specific estimand, as variations in intercurrent event strategies and historical trial designs significantly influence the estimated treatment effect and the validity of the constancy assumption.
This paper proposes novel kernel-based equivalence tests using kernel Stein discrepancy and Maximum Mean Discrepancy to rigorously assess the absence of statistically meaningful differences between distributions, addressing the limitations of traditional goodness-of-fit tests and existing parametric methods through asymptotic and bootstrap critical value approximations.
This paper details and extends a refined mortality estimation method for evaluating cancer screening programs by incorporating maximum likelihood estimation and Poisson regression offsets to utilize all available data, thereby significantly improving statistical precision and producing narrower confidence intervals compared to traditional approaches.
This paper argues for and presents an implementation of a formal representation of data analysis that externalizes an analyst's logical reasoning and assumptions, thereby enabling evaluation, visualization, and sensitivity testing of conclusions without relying solely on executable code or raw data.
Motivated by a gastric cancer trial, this paper proposes a Bayesian framework for precision clinical trials that utilizes individually weighted external data to partially borrow information for estimating subgroup effects, supported by a simulation study comparing its performance to dynamic borrowing and demonstrating its application in determining sample sizes and decision boundaries.