Big Data

Breakdown Point Theory For Implied Probability Bootstrap

Date Added: Apr 2011
Format: PDF

This paper studies robustness of bootstrap inference methods under moment conditions. In particular, the authors compare the uniform weight and implied probability bootstraps by analyzing behaviors of the bootstrap quantiles when outliers take arbitrarily large values, and derive the breakdown points for those bootstrap quantiles. The breakdown point properties characterize the situation where the implied probability bootstrap is more robust than the uniform weight bootstrap against outliers. Simulation studies illustrate their theoretical findings.