82 papers
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 proposes a mixed-frequency modeling approach to overcome the information loss inherent in temporal aggregation, demonstrating its effectiveness in predicting CAD/USD exchange rates and addressing the Meese and Rogoff puzzle.
This study demonstrates that integrating Topological Data Analysis with machine learning on foot clearance gait dynamics significantly improves the differential diagnosis between idiopathic Parkinson's disease and vascular Parkinsonism, achieving 83% accuracy and revealing sensitivity to levodopa-induced gait changes.
The paper introduces BLAST, a Bayesian transfer learning framework for high-dimensional linear regression that utilizes adaptive shrinkage and source selection to improve predictive accuracy, uncertainty quantification, and inference by effectively balancing information sharing across multiple data sources while mitigating negative transfer.
This paper extends the wgsLR model for forensic shotgun DNA sequencing to accommodate asymmetric and unknown genotyping error probabilities between trace and reference samples, demonstrating the model's robustness and providing conservative strategies for calculating weight of evidence, all implemented in an updated R package.
This paper proposes an "at-risk" transformation that binarizes macroeconomic predictors to capture the discrete nature of recessions, demonstrating that this simple approach significantly improves out-of-sample forecasting performance and makes linear models competitive with machine learning methods, particularly around the onset of U.S. recessions.
This paper introduces ForeComp, an R package designed to compare predictive accuracy using Diebold-Mariano tests with both standard and fixed-smoothing asymptotics, featuring visual diagnostics for bandwidth sensitivity and demonstrating its utility through professional forecasting applications and Monte Carlo simulations.
This paper demonstrates that a noncentral Wishart mixture of noncentral Wishart distributions with identical degrees of freedom remains a noncentral Wishart distribution, a result used to derive the finite-sample distribution for testing random effects in two-factor and general factorial design models with multivariate normal data.
This paper proposes a stable survival extrapolation framework that integrates Bayesian mortality models with flexible parametric polyhazard models to leverage external registry data as an anchor, thereby improving the robustness and interpretability of mean survival estimates in complex clinical scenarios such as breast cancer, melanoma, and cardiac arrhythmia.
The paper introduces CITS, a nonparametric statistical framework that overcomes the limitations of existing causal inference tools to accurately identify interpretable causal networks in high-resolution neural time series, as validated by both theoretical proofs and applications to large-scale mouse brain recordings.
This paper proposes a Bayesian framework for synthesizing incomplete SARS-CoV-2 data to estimate total infections and transmission dynamics, demonstrating that Hamiltonian Monte Carlo offers robust inference, mobility data enhances prediction, and informative priors combined with phase plane analysis provide superior decision support compared to restrictive assumptions.
This paper proposes a fully autonomous, state-of-the-art Integrated Air Defense system utilizing two BDI-based agents that employ meta-level plan reasoning to automatically detect threats, assess risks, and allocate weapons without manual intervention, thereby advancing network-centric warfare capabilities.
This paper proposes a series-based analytic continuation strategy using Padé approximation on the cumulant expansion of the logarithmic Laplace transform to accurately pre-distort input sequences, thereby revitalizing autoregressive processes for the efficient and precise simulation of non-Gaussian radar clutter.
This paper introduces a novel lasso-penalized threshold hidden Markov model that effectively distinguishes meaningful behavioral shifts from spurious noise, revealing that narwhals react to vessel disturbances up to 4 kilometers away by altering their movement patterns and diving deeper.
This paper develops a non-parametric sequential framework using e-values and e-processes to perform anytime-valid comparative backtests for general elicitable risk measures, offering robust performance under dependence and misspecification while introducing a modified three-zone approach for more informative model evaluation.
This paper introduces the Bayesian Hierarchical Adjustable Random Partition (BHARP) model, a self-contained framework that utilizes a finite mixture model and reversible-jump Markov chain Monte Carlo sampling to automatically identify treatment effect heterogeneity and adjust information borrowing in adaptive enrichment trials, thereby outperforming existing methods in accuracy and precision.
This study proposes a deep learning framework that utilizes an ensemble of ConvLSTM models trained on overlapping spatial grids to effectively forecast weather-related traffic crash risk by capturing complex spatiotemporal dependencies and heterogeneity, demonstrating superior performance over baseline models in North Carolina's diverse high-risk zones.
This paper introduces "segmentedness," a statistically principled metric defined as the fraction of disallowed node-pair communications, and provides a normalized estimator with a 95% confidence interval that requires only 97 uniformly sampled node pairs to accurately measure network segmentation across various models and real-world datasets.
This paper applies a probabilistic machine learning framework to analyze Collatz stopping times up to $10^7$, demonstrating that a Bayesian hierarchical Negative Binomial regression outperforms a mechanistic odd-block generator in predictive likelihood while revealing that low-order modular structure significantly drives the observed heterogeneity.
This paper employs an inverse optimization framework on NFL play-by-play data to demonstrate that coaches' historically conservative fourth-down decisions are consistent with optimizing low quantiles of future value, revealing that their risk preferences have become more tolerant over time and vary based on field position.