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Penalty, shrinkage and pretest strategies : variable selection and estimation

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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  • "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."@en

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  • "Electronic books"
  • "Electronic books"@en

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  • "Penalty, shrinkage and pretest strategies : variable selection and estimation"
  • "Penalty, shrinkage and pretest strategies : variable selection and estimation"@en
  • "Penalty, Shrinkage and Pretest Strategies Variable Selection and Estimation"@en
  • "Penalty, Shrinkage and Pretest Strategies Variable Selection and Estimation"