EFFICIENCY OF SOME SAMPLING DESIGNS FOR SPATIALLY CLUSTERED POPULATIONS

✍ Scribed by MARY C. CHRISTMAN

• Publisher: John Wiley and Sons
• Year: 1997
• Tongue: English
• Weight: 240 KB
• Volume: 8
• Category: Article
• ISSN: 1180-4009
• DOI: 10.1002/(sici)1099-095x(199703)8:2<145::aid-env249>3.0.co;2-t

✦ Synopsis

The eciencies of two sampling designs for estimating the mean of a ®xed, ®nite population are considered. The ®rst is adaptive cluster sampling (ASRS; Thompson, 1990 Journal of the American Statistical Association) which is designed to adaptively increase sampling eort in the neighbourhood of units whose observed value meets some prede®ned condition. The other is Balanced Sampling Excluding Contiguous Units (BSEC) (Hedayat, Rao and Stufken, 1988, Journal of Statistical Planning and Inference), a conventional design in which neighbouring units are deliberately excluded from being sampled under the idea that they provide little new information to the sampling eort. We consider the eect of type of neighbourhood, initial sample size, condition for adaptively sampling neighbours, and degree and extent of clustering in the population on the eciency of ASRS relative to BSEC and simple random sampling. Populations having dierent degrees of clustering are simulated using a modi®ed Neyman±Scott process. We compare the design-based variances of two estimators, a Horvitz±Thompson-type estimator and a Hansen±Hurwitztype estimator. While the Horvitz±Thompson-type estimator can have the lowest variance under some of the situations explored, it is also the most sensitive to changes in the conditions. The eciency of the estimator often comes at the cost of a large eective sample size. In general, ASRS is more ecient when the population elements are rare and highly clustered although BSEC designs are generally more ecient for a wider array of combinations of conditions.