Bootstrapping: A Nonparametric Approach to Statistical Inference.

Key Points

• This research aims to explore bootstrapping as a nonparametric method for statistical inference.
• Reviewed traditional parametric and bootstrap statistical inference methods.
• Examined applications of bootstrap confidence intervals for unknown sampling distributions.
• Evaluated bias estimation and the effectiveness of the jackknife method.
• Bootstrap methods effectively estimate bias and produce accurate confidence intervals.
• Results suggest bootstrapping is reliable even when traditional assumptions are violated.
• Monte Carlo simulations confirm the robustness of bootstrap estimates under various conditions.