Improved unbiased estimators in adaptive cluster sampling

The usual design-unbiased estimators in adaptive cluster sampling are easy to compute but are not functions of the minimal sufficient statistic and hence can be improved. Improved unbiased estimators obtained by conditioning on sufficient statistics-not necessarily minimal-are described. First, esti...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2005-02, Vol.67 (1), p.157-166
Hauptverfasser:	Dryver, Arthur L., Thompson, Steven K.
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptive sampling Cluster analysis Design-unbiased estimation Estimating techniques Estimation Estimators Exact sciences and technology Mathematical methods Mathematics Multivariate analysis Population estimates Population mean Preliminary estimates Probability and statistics Random sampling Rao-Blackwell method Sampling Sampling theory, sample surveys Sciences and techniques of general use Statistical methods Statistical variance Statistics Studies Unbiased estimators
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Beschreibung
Zusammenfassung:	The usual design-unbiased estimators in adaptive cluster sampling are easy to compute but are not functions of the minimal sufficient statistic and hence can be improved. Improved unbiased estimators obtained by conditioning on sufficient statistics-not necessarily minimal-are described. First, estimators that are as easy to compute as the usual design-unbiased estimators are given. Estimators obtained by conditioning on the minimal sufficient statistic which are more difficult to compute are also discussed. Estimators are compared in examples.
ISSN:	1369-7412 1467-9868
DOI:	10.1111/j.1467-9868.2005.00493.x