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Penalty, Shrinkage and Pretest Strategies : Variable Selection and Estimation.

Author: S Ejaz Ahmed
Publisher: Dordrecht : Springer, 2013.
Series: SpringerBriefs in statistics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models. Specifically, it considers the full model, submodel, penalty, pretest and shrinkage estimation techniques for three regression models before presenting the asymptotic properties of the non-penalty estimators and their asymptotic distributional efficiency comparisons. Further,  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: S Ejaz Ahmed
ISBN: 9783319031491 331903149X
OCLC Number: 871776414
Description: 1 online resource (122 pages).
Series Title: SpringerBriefs in statistics.

Abstract:

The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models. Specifically, it considers the full model, submodel, penalty, pretest and shrinkage estimation techniques for three regression models before presenting the asymptotic properties of the non-penalty estimators and their asymptotic distributional efficiency comparisons. Further, the risk properties of the non-penalty estimators and penalty estimators are explored through a Monte Carlo simulation study. Showcasing examples based on real datasets, the book wi.

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