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Penalty, shrinkage and pretest strategies. Variable selection and estimation. (English) Zbl 1306.62002

SpringerBriefs in Statistics. Cham: Springer (ISBN 978-3-319-03148-4/pbk; 978-3-319-03149-1/ebook). ix, 115 p. (2014).
The book’s goal is to present some shrinkage, penalty and pretest estimation techniques for different models (e.g., normal, Poisson, multiple regression, etc.). Selected penalty estimation techniques are compared with the full model, sub-model, pretest, and shrinkage estimators in the regression case. The book is dedicated to graduate students, researchers and practitioners in this field.

MSC:

62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
62J05 Linear regression; mixed models
62J07 Ridge regression; shrinkage estimators (Lasso)
62F10 Point estimation
62F12 Asymptotic properties of parametric estimators
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