Fingerprint resampling: A generic method for efficient resampling.

Area of Science:

• Computational statistics
• Statistical modeling

Background:

• Resampling methods (e.g., bootstrapping, cross-validation) often involve repetitive, computationally expensive optimization procedures on similar datasets.
• This computational burden can render resampling methods infeasible for complex problems.

Purpose of the Study:

• To develop a generic, computationally efficient solution for accelerating resampling methods.
• To reduce the computational cost associated with solving optimization problems repeatedly in resampling.

Main Methods:

• Propose a method to learn the relationship between resampled datasets and their optimization outcomes.
• Utilize predicted optima as starting values for optimization or omit the process entirely when predictions are sufficiently accurate.
• Validate the approach on analytical and real-world problems, including mixed models and extreme value distributions.

Main Results:

• The proposed method significantly increases the speed of resampling procedures.
• Achieved an average tenfold increase in the speed of resampling methods.
• Demonstrated effectiveness on both simple analytical problems and complex real-life applications.

Conclusions:

• The developed method offers a substantial computational speed-up for resampling techniques.
• Learning and predicting optimization optima is a viable strategy to overcome computational bottlenecks in statistical analysis.
• This approach enhances the feasibility of computationally demanding resampling methods in practice.