Variance estimation using list sequential scheme for unequal probability sampling
Variance estimation using list sequential scheme for unequal probability sampling
The problem of variance estimation is discussed in the light of the list sequential scheme proposed by Chao (1982), in which units are selected without replacement and with unequal probabilities. The variance is hard to estimate as it requires a large number of second-order inclusion probabilities. We prove that it is unnecessary to compute all these probabilities. We show that variance estimation needs only N numbers, where N is the population size.
variance estimation, sampling without replacement, horvitz-thompson estimator, yates-grundy estimator, inclusion probabilities, probability proportional-to-size sampling
315-323
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
September 1998
Berger, Yves G.
8fd6af5c-31e6-4130-8b53-90910bf2f43b
Berger, Yves G.
(1998)
Variance estimation using list sequential scheme for unequal probability sampling.
Journal of Official Statistics, 14 (3), .
Abstract
The problem of variance estimation is discussed in the light of the list sequential scheme proposed by Chao (1982), in which units are selected without replacement and with unequal probabilities. The variance is hard to estimate as it requires a large number of second-order inclusion probabilities. We prove that it is unnecessary to compute all these probabilities. We show that variance estimation needs only N numbers, where N is the population size.
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Published date: September 1998
Keywords:
variance estimation, sampling without replacement, horvitz-thompson estimator, yates-grundy estimator, inclusion probabilities, probability proportional-to-size sampling
Identifiers
Local EPrints ID: 34115
URI: http://eprints.soton.ac.uk/id/eprint/34115
ISSN: 0282-423X
PURE UUID: 12ad484f-885c-4616-b088-0e541bfea5f5
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Date deposited: 19 Dec 2007
Last modified: 12 Dec 2021 03:05
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