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Penalty, Shrinkage and Pretest Strategies: Variable Selection and Estimation

Penalty, Shrinkage and Pretest Strategies: Variable Selection and Estimation
Catalogue Information
Field name Details
Dewey Class 519.5 (DDC 23)
Title Penalty, Shrinkage and Pretest Strategies (EB) : Variable Selection and Estimation / by S. Ejaz Ahmed.
Author Ahmed, S. Ejaz
Other name(s) SpringerLink (Online service)
Publication Cham : Springer International Publishing, 2014.
Physical Details IX, 115 p. 6 illus. : online resource.
ISBN 9783319031491
Summary Note 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 will be useful for students and applied researchers in a host of applied fields. The book’s level of presentation and style make it accessible to a broad audience. It offers clear, succinct expositions of each estimation strategy.  More importantly, it clearly describes how to use each estimation strategy for the problem at hand.  The book is largely self-contained, as are the individual chapters, so that anyone interested in a particular topic or area of application may read only that specific chapter. The book is specially designed for graduate students who want to understand the foundations and concepts underlying penalty and non-penalty estimation and its applications. It is well-suited as a textbook for senior undergraduate and graduate courses surveying penalty and non-penalty estimation strategies, and can also be used as a reference book for a host of related subjects, including courses on meta-analysis. Professional statisticians will find this book to be a valuable reference work, since nearly all chapters are self-contained.:
Contents note Preface -- Estimation Strategies -- Improved Estimation Strategies in Normal and Poisson Models -- Pooling Data: Making Sense or Folly -- Estimation Strategies in Multiple Regression Models -- Estimation Strategies in Partially Linear Models -- Estimation Strategies in Poisson Regression Models.
System details note Online access is restricted to subscription institutions through IP address (only for SISSA internal users)
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