This paper proposes a consistent bootstrap-based distributional approximation for cube root consistent and related estimators exhibiting a Chernoff (1964)-type limiting distribution. For estimators of this kind, the standard nonparametric bootstrap is inconsistent. Our method restores consistency of the nonparametric bootstrap  by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy-to-implement resampling method for inference that is conceptually distinct from other available distributional approximations. We illustrate the applicability of our core idea with six canonical examples in statistics, machine learning, econometrics , and biostatistics. Simulation evidence is also provided .
This paper proposes a valid bootstrap-based distributional approximation for M-estimators exhibiting a Chernoff (1964)-type limiting distribution. For estimators of this kind, the standard nonparametric bootstrap is inconsistent. The method proposed herein is based on the nonparametric bootstrap , but restores consistency by altering the shape of the criterion function defining the estimator whose distribution we seek to approximate. This modification leads to a generic and easy-to-implement resampling method for inference that is conceptually distinct from other available distributional approximations. We illustrate the applicability of our results with four examples in econometrics and machine learning .
R
consistent
valid
R
cube root consistent and related estimators
M-estimators
R
Our method restores consistency of
The method proposed herein is based on
A
, but restores consistency
R
core idea with six canonical examples in statistics, machine learning, econometrics , and biostatistics. Simulation evidence is also provided
results with four examples in econometrics and machine learning