Non-probability samples provide a challenging source of information for official statistics, because the data generating mechanism is unknown. Making inference from such samples therefore requires a ...

Non-probability samples provide a challenging source of information for official statistics, because the data generating mechanism is unknown. Making inference from such samples therefore requires a ...

Survey sampling and estimation methods form the cornerstone of modern statistical inference, underpinning research across the social, medical, and natural sciences. At their heart, these methods ...

Although selecting a probability sample has been the standard for decades when making inferences from a sample to a finite population, incentives are increasing to use nonprobability samples. In a ...

Social and economic scientists are tempted to use emerging data sources like big data to compile information about finite populations as an alternative for traditional survey samples. These data ...

With a population of over 40 million users worldwide, a number doubling every year, the internet has become a medium with distinct business opportunities. To academic and commercial researchers, the ...

This repository presents a methodological framework for constructing individual prediction intervals over a finite population using data obtained from both probability and non-probability samples. The ...