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Statistics - Statistical Theory and Methods | Penalty, Shrinkage and Pretest Strategies - Variable Selection and Estimation

Penalty, Shrinkage and Pretest Strategies

Variable Selection and Estimation

Ahmed, S. Ejaz

2014, IX, 115 p. 6 illus.

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  • An important and substantial contribution to the existing knowledge on submodel, pretest and shrinkage estimation and comparison with penalty estimators
  • Nearly all the chapters are self-contained, providing theoretical and numerical solutions and featuring numerous examples based on real datasets
  • Blends together estimation and variable selection strategies for a host of applications
  • Conveys difficult ideas clearly and directly in a friendly, accessible style

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.

Content Level » Graduate

Keywords » 62J05, 62J07, 62F10, 62F12 - penalty estimation - pooling data - pretest and shrinkage estimation - regression models - variable selection

Related subjects » Computational Statistics - Statistical Theory and Methods

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