Introduces random smoothing to produce asymptotically normal estimators and Wald confidence regions for linear regression with jointly stationary-ergodic errors without long-run variance estimation.
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The authors determine separation distance limit profiles for two card shuffles and develop a spectral comparison technique illustrated on product groups and the hypercube.
URGE performs unbiased inference-time scaling for diffusion models by attaching multiplicative path weights from Girsanov estimation and resampling trajectories, with a proven equivalence to prior particle-wise SMC schemes.
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New Confidence Regions for Linear Regression Parameters with Stationary-Ergodic Dependent Errors
Introduces random smoothing to produce asymptotically normal estimators and Wald confidence regions for linear regression with jointly stationary-ergodic errors without long-run variance estimation.
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Limit Profiles for Separation Distance
The authors determine separation distance limit profiles for two card shuffles and develop a spectral comparison technique illustrated on product groups and the hypercube.
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Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures
URGE performs unbiased inference-time scaling for diffusion models by attaching multiplicative path weights from Girsanov estimation and resampling trajectories, with a proven equivalence to prior particle-wise SMC schemes.