Mathematics
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- An Improved Minimization Method: Eliminating Shift and Variance Inflation While Maintaining Marginal Balance
- Unifying Central Flow, Rod Flow, and the Edge Coupling
- Series-wise Cross-validation for Global Time Series Forecasting
- Risk-matched case-control sampling and semiparametric weight calibration for efficient biomarker assessment with binary outcomes
- Fast Functional Clustering via Closed-Form Covariance Updates
- BUMP: Brief Unexpected Mid-Interpolation Peak
- Patch Attention Wisely: Understanding Patch Length in Attention-Based Forecasting
- Bounded GARCH Processes
- Identifying Neural Dynamics Using Nonlinear Interventional State Space Models
- Doubly-robust causal inference for multi-valued treatment regimes with recurrent neural networks
- Intensity-Based Estimation of Extreme Value Models for Highly Censored Data via a Latent Poisson Process
- Joint Modeling of Conditional Mean and Variability in Multilevel Functional Data: Application to Postprandial Glucose Trajectories in the AEGIS Study
- Efficiently Estimating Cycle Statistics on Graphs via Spanning Trees
- Generalized confidence intervals for the covariate-specific overlap coefficient
- CountScan: Simultaneous Lower Bounds on Historical Watermark Matches over All Text Intervals
- Network Effect Decomposition for Causal Effect Estimation under Network Interference
- MSR-CP: A Modified Segmented Regression Approach to Change Point Estimation in Rainfall Patterns in West Bengal
- Unobserved Consideration and Consumer Welfare
- Optimal Learning in Games with Arbitrary Delays: Best-of-Both-Worlds for Self-Play and Adversarial Opponents
- Stay, Reposition, or Recharge: Battery-Aware Operation of Robotaxi Fleets
- Bayesian Spatio-temporal Modeling of Multi-Site Wastewater Surveillance Data
- Identification and Bounding of Joint Expectation over Potential Outcomes
- Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
- marp: Model-Averaged Inference for Parametric Renewal Processes in R
- Statistical and Privacy Guarantees for Deep M-Regression via Stochastic Gradient Langevin Dynamics