Adaptive Sampling Designs

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Beschreibung

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.



Autorentext

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.



Inhalt

Basic Ideas.- Adaptive Cluster Sampling.- Rao-Blackwell Modi.- Primary and Secondary Units.- Inverse Sampling Methods.- Adaptive Allocation.

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Produktinformationen

Titel
Adaptive Sampling Designs
Untertitel
Inference for Sparse and Clustered Populations
Autor
EAN
9783642336577
ISBN
978-3-642-33657-7
Format
E-Book (pdf)
Hersteller
Springer Berlin Heidelberg
Herausgeber
Springer
Genre
Grundlagen
Veröffentlichung
22.10.2012
Digitaler Kopierschutz
Wasserzeichen
Dateigrösse
0.79 MB
Anzahl Seiten
70
Jahr
2012
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