1,205 records
- Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions
- Causal discovery under mean independence and linearity
- Augmented transfer regression learning for completely missing covariates
- Multiscale Euclidean Network Trajectories: Second-Moment Geometry, Attribution, and Change Points
- Tests for white noise via asymptotically independent U-statistics in high-dimensions
- Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation
- A renormalization-group inspired lattice-based framework for piecewise generalized linear models
- Tests for constancy of model parameters Over time
- Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
- Imbalanced Classification under Capacity Constraints
- Free Decompression with Algebraic Spectral Curves
- More Permutations Do Not Always Increase Power: Non-monotonicity in Monte Carlo Permutation Tests
- Perturbation is All You Need for Extrapolating Language Models
- Fast and accurate conditioning for large-scale Gaussian process prediction problems
- Tweedie-based nonparametric estimation for semicontinuous mixed densities
- Structural and Lagrangian properties of analogue ensembles to characterize multifractality of stochastic processes
- Adaptive Targeted Maximum Likelihood Estimation of the Mean Potential Outcome under a Treatment Rule
- Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
- Extrapolation in Statistical Learning with Extreme Value Theory
- Mean Testing under Truncation beyond Gaussian
- The Pearson IV distribution: Random variate generation and applications
- L-Estimation of Population Quantiles Using Ranked Set Sampling
- Self-Normalized Martingales and Uniform Regret Bounds for Linear Regression
- Beyond Independence: on Jointly Normal Priors in Bayesian Inversion
- Concentration and Calibration in Predictive Bayesian Inference
- Gradient Regularized Newton Boosting Trees with Global Convergence
- Decentralized Proximal Stochastic Gradient Langevin Dynamics
- Quenched Amplification and Tail Shaping in Networked Systems with Memory and Regime Switching
- Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particles
- Single Change-Point Detection via Energy Distance with Application to Genomic Data
- Kernel-based independence and mean independence tests for weakly dependent data
- Modeling Stock Returns and Volatility Using Bivariate Gamma Generalized Laplace Law
- A unified perspective on fine-tuning and sampling with diffusion and flow models
- ARMA approximation of a Non-separable Spatio-Temporal Model with Fractional Smoothnesses in Space and Time
- Principled Estimation and Prediction with Competing Risks: a Bayesian Nonparametric Approach
- Elite-Driven Support Vector Machines for Classification
- Geometry of tail allocation in conformal prediction intervals
- Generalized Local Polynomial Regression with Decomposed Context-Aware Kernels
- CBARA: Covariate-Balanced-and-Adjusted Response-Adaptive Randomization
- Deflation-Free Optimal Scoring
- Model-agnostic information transfer and fusion for classification with label noise
- Principal Component Based Estimation of Finite Population Mean under Multicollinearity
- A semiparametric autorregresive spatial prediction model
- Neyman Jackknife: Design-Based Variance Estimation for Causal Inference under Interference
- Theoretical guarantees for stochastic gradient sampling methods via Gaussian convolution inequalities
- Nonparametric Instrumental Variable Analysis Without Structural Equations: Debiased Inference on Functionals of Inverse Problems with No Solutions
- Large-Sample Bayesian Approximations for Privatized Data
- Weak Moment Methods for Statistical Inference: with an Application to Robust Estimation
- Bayesian change-plane regression
- Learning Curves and Benign Overfitting of Spectral Algorithms in Large Dimensions