Can I read Regression Estimators: A Comparative Study (Statistical Modeling and Decision Science) on EtoBox?

Regression Estimators: A Comparative Study (Statistical Modeling and Decision Science) by Marvin H. J. Gruber, Gerald J. Lieberman and Ingram Olkin (Auth.) is a nonfiction available to read on EtoBox.

What is Regression Estimators: A Comparative Study (Statistical Modeling and Decision Science) about?

An examination of mathematical formulations of ridge-regression-type estimators points to a curious observation: estimators can be derived by both Bayesian and Frequentist methods. In this updated and expanded edition of his 1990 treatise on the subject, Marvin H. J. Gruber presents, compares, and contrasts the development and properties of ridge-type estimators from these two philosophically different points of view. The book is organized into five sections. Part I gives a historical survey of the literature and summarizes basic ideas in matrix theory and statistical decision theory. Part II explores the mathematical relationships between estimators from both Bayesian and Frequentist points of view. Part III considers the efficiency of estimators with and without averaging over a prior distribution. Part IV applies the methods and results discussed in the previous two sections to the Kalman Filter, analysis of variance models, and penalized splines. Part V surveys recent developments in the field. These include efficiencies of ridge-type estimators for loss functions other than squared error loss functions and applications to information geometry. Gruber also includes an updated h

Who reads Regression Estimators: A Comparative Study (Statistical Modeling and Decision Science)?

Author

Marvin H. J. Gruber, Gerald J. Lieberman and Ingram Olkin (Auth.)

Publisher

Elsevier Inc, Academic Press

Published

1990

Language

EN

ISBN

9781483260976

Category

nonfiction

Subjects

Mathematics, Science, Stem