Adaptive Sampling Designs: Inference for Sparse and Clustered Populations

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Springer Science & Business Media, Oct 22, 2012 - Mathematics - 70 pages
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This book aims to provide an overview of some adaptive techniques used in estimating parameters for finite populations where the sampling at any stage depends on the sampling information obtained to date. The sample adapts to new information as it comes in. These methods are especially used for sparse and clustered populations.
Written by two acknowledged experts in the field of adaptive sampling.

 

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Contents

1 Basic Ideas
1
2 Adaptive Cluster Sampling
11
3 RaoBlackwell Modifications
27
4 Primary and Secondary Units
37
5 Inverse Sampling Methods
49
6 Adaptive Allocation
61
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About the author (2012)

George Seber is an Emeritus Professor of Statistics at Auckland University, New Zealand. He is an elected Fellow of the Royal Society of New Zealand and recipient of their Hector medal in Science. He has authored or coauthored 13 books and 77 research articles on a wide variety of topics including linear and nonlinear models, multivariate analysis, adaptive sampling, genetics, epidemiology, and statistical ecology.

Mohammad Salehi is a Professor of Statistics at Isfahan University of Technology, Iran. Currently, he is also a Professor of Statistics and Director of the Statistical Consulting Unit at Qatar University, Qatar, and has published extensively in the field of adaptive sampling.

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