Resamplinguks

Resamplinguks is a family of resampling procedures designed to estimate sampling distributions of statistics from complex, possibly dependent data. The approach extends conventional resampling by incorporating adaptive weighting to preserve structural features such as temporal, spatial, or stratified dependence while generating resamples.

Methodology: Starting from a dataset, resamplinguks creates many resamples by selecting blocks or clusters with probabilities determined by a kernel-weighted scheme. Weights reflect local data density, autocorrelation, or stratification, and the resample size may be adjusted to improve estimator stability. This yields bootstrap-like estimates that respect the dependence structure.

Variants and technical notes: Common variants include block-based resampling, subsampling, and permutation-based versions. A key feature of resamplinguks is the use of a kernel function to assign weights to observations or blocks, enabling smooth tuning of bias and variance. The method often requires selecting a block length or bandwidth parameter.

Applications: Used in econometrics, environmental science, epidemiology, and time-series forecasting to estimate confidence intervals, standard errors, or p-values for statistics under complex dependence.

Advantages and limitations: Advantages include better preservation of dependence, flexibility across data types, and improved finite-sample performance in certain settings. Limitations include increased computational cost, sensitivity to kernel choice and block length, and potential overfitting if parameters are not chosen carefully.

See also: bootstrap, block bootstrap, subsampling, permutation tests, kernel methods.