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Size constrained unequal probability sampling with a non-integer sum of inclusion probabilities

Grafström, Anton and Qualité, Lionel and Tillé, Yves and Matei, Alina (2012). Size constrained unequal probability sampling with a non-integer sum of inclusion probabilities. Electronic journal of statistics. 6, 1477-1489
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Official URL: http://dx.doi.org/10.1214/12-EJS719

Abstract

More than 50 methods have been developed to draw unequal probability samples with fixed sample size. All these methods require the sum of the inclusion probabilities to be an integer number. There are cases, however, where the sum of desired inclusion probabilities is not an integer. Then, classical algorithms for drawing samples cannot be directly applied. We present two methods to overcome the problem of sample selection with unequal inclusion probabilities when their sum is not an integer and the sample size cannot be fixed. The first one consists in splitting the inclusion probability vector. The second method is based on extending the population with a phantom unit. For both methods the sample size is almost fixed, and equal to the integer part of the sum of the inclusion probabilities or this integer plus one.

Authors/Creators:Grafström, Anton and Qualité, Lionel and Tillé, Yves and Matei, Alina
Title:Size constrained unequal probability sampling with a non-integer sum of inclusion probabilities
Series/Journal:Electronic journal of statistics (1935-7524)
Year of publishing :2012
Volume:6
Page range:1477-1489
Publisher:Institute of Mathematical Statistics, Bernoulli Society for Mathematical Statistics and Probability
ISSN:1935-7524
Language:English
Publication Type:Journal article
Refereed:Yes
Article category:Scientific peer reviewed
Version:Published version
Full Text Status:Public
Agris subject categories.:U Auxiliary disciplines > U10 Mathematical and statistical methods
X Agricola extesions > X10 Mathematics and statistics
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 101 Mathematics > 10106 Probability Theory and Statistics
Keywords:survey sampling, maximum entropy, splitting method
URN:NBN:urn:nbn:se:slu:epsilon-e-776
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-e-776
Additional ID:
Type of IDID
DOI10.1214/12-EJS719
ID Code:9317
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: Skogsbiblioteket Umeå
Deposited On:13 Dec 2012 11:28
Metadata Last Modified:02 Dec 2014 10:53

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