| DC Field | Value | Language |
|---|
| dc.contributor.author | Wakefield, Jon | - |
| dc.date.accessioned | 2021-04-20T04:40:30Z | - |
| dc.date.available | 2021-04-20T04:40:30Z | - |
| dc.date.issued | 2013 | - |
| dc.identifier.isbn | 978-1-4419-0925-1 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/105 | - |
| dc.description | As a preparation for this book, the reader is assumed to have a grasp of calculus
and linear algebra and have taken first courses in probability and statistical theory.
The content of this book is as follows. An introductory chapter describes a number
of motivating examples and discusses general issues that need consideration before
a regression analysis is carried out. This book is then broken into five parts: I, In-
ferential Approaches; II, Independent Data; III, Dependent Data; IV, Nonparametric
Modeling; V, Appendices. The first two chapters of Part I provide descriptions of the
frequentist and Bayesian approaches to inference, with a particular emphasis on the
rationale of each approach and a delineation of situations in which one or the other
approach is preferable. The third chapter in Part I discusses model selection and
hypothesis testing. Part II considers independent data and contains three chapters on
the linear model, general regression models (including generalized linear models),
and binary data models. The two chapters of Part III consider dependent data
with linear models and general regression models. Mixed models and generalized
estimating equations are the approaches to inference that are emphasized. Part IV
contains three chapters on nonparametric modeling with an emphasis on spline and
kernel methods. The examples and simulation studies of this book were almost
exclusively carried out within the freely available R programming environment. The
code for the examples and figures may be found at: | en_US |
| dc.description.abstract | The past 25 years have seen great advances in both Bayesian and frequentist
methods for data analysis. The most significant advance for the Bayesian approach
has been the development of Markov chain Monte Carlo methods for estimating
expectations with respect to the posterior, hence allowing flexible inference and
routine implementation for a wide range of models. In particular, this development
has led to the more widespread use of hierarchical models for dependent data. With
respect to frequentist methods, estimating functions have emerged as a unifying
approach for determining the properties of estimators. Generalized estimating
equations provide a particularly important example of this methodology that allows
inference for dependent data.
The aim of this book is to provide a modern description of Bayesian and
frequentist methods of regression analysis and to illustrate the use of these methods
on real data. Many books describe one or the other of the Bayesian or frequentist
approaches to regression modeling in different contexts, and many mathematical
statistics texts describe the theory behind Bayesian and frequentist approaches
without providing a detailed description of specific methods. References to such
texts are given at the end of Chaps. 2 and 3. Bayesian and frequentist methods are
not viewed here as competitive, but rather as complementary techniques, and in this
respect this book has some uniqueness. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Springer | en_US |
| dc.subject | Regression Methods | en_US |
| dc.subject | Statistics | en_US |
| dc.title | Bayesian and Frequentist Regression Methods | en_US |
| dc.title.alternative | Springer Series in Statistics | en_US |
| dc.type | Book | en_US |
| Appears in Collections: | ARTS & SCIENCE
|
