Friday, 31 January 2014

[U878.Ebook] Free PDF Adaptive Sampling Designs: Inference for Sparse and Clustered Populations (SpringerBriefs in Statistics), by G. A. F. Seber, Mohammad M. S

Free PDF Adaptive Sampling Designs: Inference for Sparse and Clustered Populations (SpringerBriefs in Statistics), by G. A. F. Seber, Mohammad M. S

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Adaptive Sampling Designs: Inference for Sparse and Clustered Populations (SpringerBriefs in Statistics), by G. A. F. Seber, Mohammad M. S

Adaptive Sampling Designs: Inference for Sparse and Clustered Populations (SpringerBriefs in Statistics), by G. A. F. Seber, Mohammad M. S



Adaptive Sampling Designs: Inference for Sparse and Clustered Populations (SpringerBriefs in Statistics), by G. A. F. Seber, Mohammad M. S

Free PDF Adaptive Sampling Designs: Inference for Sparse and Clustered Populations (SpringerBriefs in Statistics), by G. A. F. Seber, Mohammad M. S

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Adaptive Sampling Designs: Inference for Sparse and Clustered Populations (SpringerBriefs in Statistics), by G. A. F. Seber, Mohammad M. S

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.

  • Sales Rank: #4467175 in Books
  • Brand: Brand: Springer
  • Published on: 2012-10-31
  • Released on: 2012-10-31
  • Original language: English
  • Number of items: 1
  • Dimensions: 9.25" h x .19" w x 6.10" l, .0 pounds
  • Binding: Paperback
  • 70 pages
Features
  • Used Book in Good Condition

Review

From the book reviews:

“The book is suitable for a reader interested in adaptive sampling designs and estimators based on thorough mathematical theory.” (Adriana Horn�kov�, Technometrics, Vol. 56 (2), May, 2014)

From the Back Cover

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.

About the Author

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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