219 papers
This paper addresses the underexplored challenge of variable selection for linear regression imputation in survey data by defining an optimal model via an oracle loss function, analyzing the consequences of model misspecification, and proposing a methodological framework for constructing asymptotically valid and optimal confidence intervals.
The paper introduces KRAFTY, a novel multi-view clustering framework that utilizes a transposed Khatri-Rao matrix to represent joint clusters in orthogonal subspaces, enabling accurate recovery of true cluster structures and effective model selection even when the number of joint clusters exceeds the sum of clusters in individual views.
This paper proposes a new least trimmed squares regression method that simultaneously handles missing values, resists both casewise and cellwise outliers, accommodates skewed distributions, and enables robust out-of-sample predictions.
This paper presents a scalable multi-fidelity tensor emulator that integrates low- and high-resolution Arctic sea ice simulation data using tensor decomposition and Gaussian processes to efficiently generate accurate predictions with well-calibrated uncertainty, significantly outperforming single-fidelity approaches in reducing both computational cost and prediction error.
This paper proposes a robust estimation method for diffusion processes using -divergence to mitigate the impact of outliers in high-frequency data, establishing its asymptotic properties and bounded influence functions through Gaussian approximation of transition densities.
This paper proposes a unified pipeline utilizing ANOVA Simultaneous Component Analysis (ASCA) to model and interpret cyclostationary time series by effectively handling multivariate data, multiple time scales, and autocorrelation, as demonstrated through case studies on mountain lake water temperatures and airborne pollen trends.
This paper proposes a multichannel profile covariance (MPC) control chart based on functional graphical models and a nonparametric combination of likelihood-ratio tests to effectively detect subtle, sparse changes in covariance structures that traditional mean-focused methods often miss.
This paper introduces TS-BOSS, a scalable, score-based algorithm for learning causal structures in multivariate time series that extends the Best Order Score Search framework with dynamic Bayesian networks and grow-shrink trees, demonstrating superior performance in high auto-correlation regimes compared to standard constraint-based methods.
This paper introduces a parsimonious Levy-driven graph supOU process for modeling high-dimensional time series with both short- and long-range dependence, develops a consistent generalized method of moments estimator for it, and validates the approach through simulations and an empirical study of European wind capacity factors.
This paper introduces the Discrete Chi-Square Method (DCM), a robust forecasting tool based on the Gauss-Markov theorem and the Window Dimension Effect, which overcomes the limitations of traditional frequency-domain methods to successfully model and forecast complex time series, including El Niño data from 1870 to 2024.
This paper establishes a unified statistical framework demonstrating that synthetic augmentation in imbalanced learning is not universally beneficial, revealing that its efficacy and optimal quantity depend on local data symmetry and generator alignment, and proposing a Validation-Tuned Synthetic Size (VTSS) strategy to empirically determine the best augmentation level.
This paper proposes a new paradigm for causal discovery that leverages crowdsourcing, expert elicitation, and LLM-based simulation to aggregate fragmented knowledge from multiple agents into a comprehensive global causal structure unattainable by any single entity.
This paper proposes a novel, implementable adaptive parameter selection strategy for kernel-based gradient descent that integrates bias-variance analysis with the splitting method and empirical effective dimension to achieve optimal generalization error bounds across diverse kernels, target functions, and error metrics.
CausalMix is a variational generative framework that bridges the gap between distributional realism and causal controllability by enabling independent manipulation of overlap, confounding, and treatment effect heterogeneity in synthetic mixed-type tabular data for rigorous causal inference study design and validation.
This paper investigates the projected isotropic normal distribution, deriving new properties and practical approximations for its mean resultant statistic to facilitate phase-based analysis of EEG signals recorded during flash stimulation.
The paper introduces the Pivotal Information Criterion (PIC), a continuous optimization method that dynamically selects its penalty parameter at the detection boundary to overcome the high false discovery rates and computational infeasibility of traditional criteria like BIC and AIC in high-dimensional settings.
This paper introduces "Bayesian Adversarial Privacy," a new quantitative framework that offers a more contextual and rigorous alternative to differential privacy and statistical disclosure theory by grounding disclosure decisions in a prior viewpoint derived from Bayesian decision theory.
This paper establishes a principled theoretical framework for density aggregation by demonstrating that normalized generalized means with order are the only rules guaranteeing systematic improvements in log-likelihood over individual distributions, thereby providing a unified justification for the widespread use of linear and geometric pooling in Deep Ensembles.
This paper proposes a model-free spectral inference method based on eigengap ratios that effectively determines the number of communities in both dense and sparse networks with a diverging number of communities, without requiring explicit model fitting or parameter tuning.