Resampling

Resampling is a statistical technique involving the repeated drawing of samples from the original dataset to create multiple versions of the data. It's employed to assess the uncertainty of statistics, refine predictions, and improve the accuracy and reliability of analytical results. It essentially involves drawing new samples from an existing dataset, typically with replacement (e.g., bootstrapping) or without replacement (e.g., cross-validation). These new samples are then used to recalculate the statistic of interest, providing insights into its variability and potential biases. resampling allows for the estimation of statistical properties like standard errors, confidence intervals, and p-values without relying on restrictive distributional assumptions. It is very effective where the underlying distributions are unknown, non-normal or where an estimation error must be minimised.

Resampling meaning with examples

• In machine learning, resampling methods like cross-validation are used to evaluate a model's performance on unseen data. Data is split into subsets, and the model is trained and tested on different combinations of these subsets. This allows for a more robust assessment of the model's generalization ability and helps in identifying overfitting.
• Researchers studying ecological data use bootstrapping, a type of resampling, to estimate confidence intervals for population parameters. By repeatedly sampling with replacement from the observed data, they create a distribution of the statistic. This is a great method where sample sizes are small and assumptions of normality can be easily challenged.
• When analyzing financial time series, resampling can be used to simulate various market scenarios and assess the potential risks associated with investment strategies. This could involve generating a large number of 'what if' scenarios using historical data and performing the simulations.
• In image processing, resampling techniques are used to alter the resolution of an image. This can be done to resize it for different displays or to apply image-based features to a new dataset, improving the efficiency of computation for algorithms, or allowing the data to be converted into a more useable format