A Bayesian Bootstrap for a Finite Population

A nonparametric Bayesian prediction interval for a finite population mean

Given a sample from a finite population, we provide a nonparametric Bayesian prediction interval for a finite population mean when a standard normal assumption may be tenuous. We will do so using a Dirichlet process (DP), a nonparametric Bayesian procedure which is currently receiving much attention. An asymptotic Bayesian prediction interval is well known but it does not incorporate all the fe...

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...

A Direct Bootstrap Method for Complex Sampling Designs From a Finite Population

In complex designs, classical bootstrap methods result in a biased variance estimator when the sampling design is not taken into account. Resampled units are usually rescaled or weighted in order to achieve unbiasedness in the linear case. In the present article, we propose novel resampling methods that may be directly applied to variance estimation. These methods consist of selecting subsample...

Bayesian Finite Population Imputation for Data Fusion

In data fusion, data owners seek to combine datasets with disjoint observations and distinct variables to estimate relationships among the variables. One approach is to concatenate the files, specify models relating the variables not jointly observed, and use the models to generate multiple imputations of the missing data. We show that the standard multiple imputation estimator of the sampling ...

Perishable Inventory System with a Finite Population and Repeated Attempts