Ahmed, S. Ejaz 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. Reviewer: Marina Gorunescu (Craiova) Cited in 31 Documents 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 Keywords:estimation strategy; normal models; Poisson models; pooling data; multiple regression models; partially linear models; Poisson regression models PDFBibTeX XMLCite \textit{S. E. Ahmed}, Penalty, shrinkage and pretest strategies. Variable selection and estimation. Cham: Springer (2014; Zbl 1306.62002) Full Text: DOI