On a generalization of Poisson sampling
2010 (English)In: Journal of Statistical Planning and Inference, ISSN 0378-3758, Vol. 140, no 4, 982-991 p.Article in journal (Other academic) Published
In real-time sampling, the units of a population pass a sampler one by one. Alternatively the sampler may successively visit the units of the population. Each unit passes only once and at that time it is decided whether or not it should be included in the sample. The goal is to take a sample and efficiently estimate a population parameter. The list sequential sampling method presented here is called correlated Poisson sampling. The method is an alternative to Poisson sampling, where the units are sampled independently with given inclusion probabilities. Correlated Poisson sampling uses weights to create correlations between the inclusion indicators. In that way it is possible to reduce the variation of the sample size and to make the samples more evenly spread over the population. Simulation shows that correlated Poisson sampling improves the efficiency in many cases.
Place, publisher, year, edition, pages
2010. Vol. 140, no 4, 982-991 p.
Correlated Bernoulli sampling, Correlated Poisson sampling, Horvitz–Thompson ratio estimator, Inclusion probabilities, List sequential sampling, Real-time sampling, Simulation, Splitting method
Probability Theory and Statistics
Research subject Mathematical Statistics
IdentifiersURN: urn:nbn:se:umu:diva-8389DOI: 10.1016/j.jspi.2009.09.024ISI: 000273659900011OAI: oai:DiVA.org:umu-8389DiVA: diva2:148060
Även utgiven som: Research Report in Mathematical Statistics, ISSN 1653-0829, 2007:2.2008-01-202008-01-202015-10-02Bibliographically approved