文档介绍：Jon Wakefield
Bayesian and Frequentist
Regression Methods
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Preface
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 petitive, but rather plementary techniques, and in this
respect this book has some uniqueness.
In embarking on the writing of this book, I have been influenced by many current
and former colleagues. My early training was in the Mathematics Department at
the University of Nottingham and my first permanent academic teaching position
was in the Mathematics Department at Imperial College of Science, Technology
and Medicine in London. During this period I was introduced to the Bayesian
paradigm and was greatly influenced by Adrian Smith, both as a lecturer and as
a .