Estimated Sampling Distributions: The Bootstrap and Competitors

Smoothed Bootstrap and Jackboot Sampling Smoothed Bootstrap and Jackboot Sampling

We propose a bootstrap sampling method jackboot sampling This provides more accu rate inferences than ordinary bootstrap sampling better con dence interval coverage and less biased or unbiased standard errors The method is simple to implement We also prescribe a smoothing parameter for use in smoothed bootstrapping using or dinary kernel smoothing The e ect is similar to that of jackboot sampling

Sampling Distributions Sampling Distributions

Sampling and Sampling Distributions

A sample is a group of objects or readings taken from a population for counting or measurement. We shall distinguish between two kinds of populations — finite populations and infinite populations. The sample must be random, meaning that all possible samples of a particular size must have equal probabilities of being chosen from the population. This will prevent bias in the sampling process. Mos...

The bootstrap and finite population sampling.

Synthetic populations are used to study methods for adapting Efron’s bootstrap estimation technique to finite population sampling. Of particular interest is the extention of these methods to two-stage cluster sampling. Using simulations based on five artificial populations, two variations of bootstrap estimators and two Taylor series variance estimators for a ratio estimator are compared by mea...

Thompson sampling with the online bootstrap

Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its performance is dependent on the model fit to the observed data. We introduce bootstr...