A01=Dani Gamerman
A01=Francisco Louzada
A01=Helio S. Migon
advanced Bayesian frequentist comparison
Asymptotic Confidence Region
Author_Dani Gamerman
Author_Francisco Louzada
Author_Helio S. Migon
Bayes Factor
Bayesian And Frequentist Inference
Bias Correction
bias correction techniques
Category=PBT
Comparative Approach To Inference
Conditional Expectation
Credibility Interval
Em Algorithm
Empirical Bayes And Penalized Likelihoods
Empirical Bayes Estimators
empirical Bayes methods
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Fisher Information Measures
HPD Credibility Interval
HPD Interval
Hypothesis Testing
hypothesis testing strategies
Intrinsic Bayes Factor
Jeffreys Prior
linear regression modelling
Marginal Likelihood
Maximum Likelihood Estimator
Maximum Likelihood Ratio Test
Method Of Moments
method of moments inference
Minimal Sufficient
Minimal Sufficient Statistics
Minimal Training Sample
Non-informative Prior
penalised likelihood estimation
Pivotal Quantity
Poisson Exponential Distribution
Posterior Distribution
Predictive Distribution
Prior Distribution
Regression Models
Standard Inference Courses
Ump Test
UMVU Estimator
Product details
- ISBN 9781439878804
- Weight: 680g
- Dimensions: 156 x 234mm
- Publication Date: 03 Sep 2014
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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A Balanced Treatment of Bayesian and Frequentist Inference
Statistical Inference: An Integrated Approach, Second Edition presents an account of the Bayesian and frequentist approaches to statistical inference. Now with an additional author, this second edition places a more balanced emphasis on both perspectives than the first edition.
New to the Second Edition
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- New material on empirical Bayes and penalized likelihoods and their impact on regression models
- Expanded material on hypothesis testing, method of moments, bias correction, and hierarchical models
- More examples and exercises
- More comparison between the approaches, including their similarities and differences
-
Designed for advanced undergraduate and graduate courses, the text thoroughly covers statistical inference without delving too deep into technical details. It compares the Bayesian and frequentist schools of thought and explores procedures that lie on the border between the two. Many examples illustrate the methods and models, and exercises are included at the end of each chapter.
Helio S. Migon (Universidade Federal do Rio de Janeiro, Brazil) (Author) , Dani Gamerman (Universidade Federal do Rio de Janeiro, Brazil) (Author) , Francisco Louzada (Universidade Federal de Sao Carlos, Brazil) (Author)
