Jackknife resampling is a statistical technique used to estimate the bias and variance of a statistical estimator. It involves systematically leaving out one observation from the dataset at a time and calculating the estimator on the reduced dataset. This process is repeated for each observation, and the results are then used to compute the overall estimate, along with its variance and bias. ### Key Steps in Jackknife Resampling: 1. **Original Estimate Calculation:** Calculate the estimator (e.g.
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