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Adaptive Sampling Designs

Inference for Sparse and Clustered Populations

  • George A.F. Seber
  • Mohammad M. Salehi

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Table of contents

  1. Front Matter
    Pages i-ix
  2. George A. F. Seber, Mohammad M. Salehi
    Pages 1-10
  3. George A. F. Seber, Mohammad M. Salehi
    Pages 11-26
  4. George A. F. Seber, Mohammad M. Salehi
    Pages 27-36
  5. George A. F. Seber, Mohammad M. Salehi
    Pages 37-47
  6. George A. F. Seber, Mohammad M. Salehi
    Pages 49-60
  7. George A. F. Seber, Mohammad M. Salehi
    Pages 61-70

About this book

Introduction

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.

Keywords

Adaptive allocation Adaptive sampling Inverse sampling Sampling rare Sparse and clustered populations Stratified adaptive sampling Two-stage sampling

Authors and affiliations

  • George A.F. Seber
    • 1
  • Mohammad M. Salehi
    • 2
  1. 1., Department of StatisticsUniversity of AucklandAucklandNew Zealand
  2. 2., Mathematics, Statistics and PhysicsQatar UniversityDohaQatar

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-33657-7
  • Copyright Information The Author(s) 2013
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-642-33656-0
  • Online ISBN 978-3-642-33657-7
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site
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