Premium
Conditional and Restricted Pareto Sampling: Two New Methods for Unequal Probability Sampling
Author(s) -
BONDESSON LENNART
Publication year - 2010
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2010.00700.x
Subject(s) - mathematics , pareto principle , pareto interpolation , sampling (signal processing) , probability sampling , sampling design , statistics , lomax distribution , simple random sample , slice sampling , inference , pareto distribution , importance sampling , econometrics , generalized pareto distribution , monte carlo method , computer science , extreme value theory , artificial intelligence , population , demography , filter (signal processing) , sociology , computer vision
. Two new unequal probability sampling methods are introduced: conditional and restricted Pareto sampling. The advantage of conditional Pareto sampling compared with standard Pareto sampling, introduced by Rosén ( J. Statist. Plann. Inference , 62, 1997, 135, 159), is that the factual inclusion probabilities better agree with the desired ones. Restricted Pareto sampling, preferably conditioned or adjusted, is able to handle cases where there are several restrictions on the sample and is an alternative to the recent cube method for balanced sampling introduced by Deville and Tillé ( Biometrika , 91, 2004, 893). The new sampling designs have high entropy and the involved random numbers can be seen as permanent random numbers.