Description 
1 online resource (299 pages) 
Series 
Routledge Library Editions: Econometrics Ser 

Routledge Library Editions: Econometrics Ser

Contents 
Cover; Half Title; Title Page; Copyright Page; Contents; Acknowledgements; Preface; Notation and abbreviations; 1 Introduction to Bayes and empirical Bayes methods; 1.1 The problem, Bayes conventional and empirical Bayes methods; 1.2 An introduction to Bayes techniques; 1.3 Bayes point estimation: one parameter; 1.4 Bayes decisions between k simple hypotheses; 1.5 Bayes decisions between two composite hypotheses; 1.6 Bayes estimation of vector parameters; 1.7 Bayes decisions and multiple independent observations; 1.8 Empirical Bayes methods; 1.9 An example: EB estimation in the Poisson case 

1.10 The goodness of EB procedures1.11 Smooth EB estimates; 1.12 Approximate Bayes and empirical Bayes methods; 1.13 Concomitant variables; 1.14 Competitors of EB methods; 2 Estimation of the prior distribution; 2.1 Introduction; 2.2 Identifiability; 2.3 Parametric G families; 2.4 Finite mixtures; 2.5 General mixtures: identifiability; 2.6 Identifiability of multiparameter mixtures; 2.7 Determination of G(); 2.8 Estimation of G: parametric G families; 2.9 Estimation of G: finite approximation of G; 2.10 Estimation of G: continuous mixtures; 2.11 Estimation of G: miscellaneous methods 

2.12 Estimation with unequal component sample sizes3 Empirical Bayes point estimation; 3.1 Introduction; 3.2 Asymptotic optimality; 3.3 Robustness with respect to the prior distribution; 3.4 Simple EB estimates; 3.5 EB estimation through estimating G; 3.6 Linear EB estimation; 3.7 EB estimation for special univariate distributions: one current observation; 3.8 EB estimation with multiple current observations: one parameter; 3.9 Unequal mi, = 1: application to particular distributions; 3.10 Nonparametric EB estimation; 3.11 Assessing performance of EBEs in practice 

4 Empirical Bayes point estimation: vector parameters4.1 Introduction; 4.2 Locationscale estimation; 4.3 Quantile estimation; 4.4 The multivariate normal distribution; 4.5 The multinomial distribution; 4.6 Linear Bayes and EB, and subsets of parameters; 4.7 Concomitant variables; 5 Testing of hypotheses; 5.1 Introduction; 5.2 Two simple hypotheses, oneparameter problems; 5.3 k = 3 simple hypotheses; 5.4 Two composite hypotheses; 6 Bayes and empirical Bayes interval estimation; 6.1 Introduction; 6.2 Intervals for single parameters; 6.3 The multiparameter case: region estimators 

6.4 Bayes statistical tolerance regions6.5 EB tolerance regions; 6.6 Other approaches to EB interval and region estimation; 7 Alternatives to empirical Bayes; 7.1 Introduction; 7.2 The multiparameter full Bayesian approach; 7.3 Likelihoodbased approaches; 7.4 Compound estimation and decision theory; 7.5 General discussion; 8 Applications of EB methods; 8.1 Introduction; 8.2 Examples with normal data distributions; 8.3 Examples involving standard discrete distributions; 8.4 Miscellaneous EB applications; References; Author index; Subject index 
Notes 
Print version record 
Subject 
Bayesian statistical decision theory.


Bayesian statistical decision theory

Form 
Electronic book

Author 
Lwin, T

ISBN 
9781351140638 

1351140639 

9781351140621 

1351140620 

9781351140614 

1351140612 

9781351140645 

1351140647 
