An Accurate Percentile Method for Parametric Inference Based on Asymptotically Biased Estimators
Percentile-based inference that corrects asymptotic bias to deliver accurate confidence sets in finite samples.
Percentile-based inference that corrects asymptotic bias to deliver accurate confidence sets in finite samples.
Wrapper method for sparse learning with competitive finite-sample performance.
Addresses unbiased estimation around phase transitions in high-dimensional regimes.
Practical guidance for accurate parametric inference when sample sizes are limited.