The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

A sampling algorithm is a procedure used to select a sample. If the sampling design is
known and if the population size is not too large, a sample can be selected directly by …

Supplement to “Approaches to Improving Survey-Weighted Estimates”. The Supplementary
Material includes the density plots of the size variables, and dot plots summarizing the …

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 …

Pareto sampling was introduced by Rosén in the late 1990s. It is a simple method to get a
fixed size πps sample though with inclusion probabilities only approximately as desired …

The problem of variance estimation in the presence of singly imputed data has attracted a lot
of attention in the last three decades. Treating the imputed values as if they were observed …

The most common way to treat item nonresponse in surveys is to replace a missing value by
a plausible value constructed on the basis of fully observed variables. Treating the imputed …

Inclusion probability proportional to size (IPPS) sampling designs are often used in surveys,
especially for the selection of primary sampling units in the context of multi-stage sampling …

This paper is devoted to rejective sampling. We provide an expansion of joint inclusion
probabilities of any order in terms of the inclusion probabilities of order one, extending …

A new and very fast method of bootstrap for sampling without replacement from a finite
population is proposed. This method can be used to estimate the variance in sampling with …