Predictive Inference
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€78.99
A01=Seymour Geisser
Aid Virus
approxim
ation
Author_Seymour Geisser
Bayesian statistics
Binary Diagnostic Test
Category=PBT
Conditional Predictive Density
Confidence Coefficient
Data Set
Data Sets
De Finetti's Theorem
De Finetti’s Theorem
density
Discordancy Indices
Distribution Function
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
ete
exam
Hellinger Distance
Highest Probability Density Regions
HPD Interval
Interim Analyses
interim data analysis
Kullback Divergence
Loss Function
Measurement Error Model
multivariate analysis
nonparametric models
param
ple
Posterior Distribution
Posterior Odds
Predictive Confidence Interval
Predictive Density
Predictive Distribution
Predictive Distribution Function
predictive methods in social sciences
Predictive Probabilities
Predictive Probability Function
Prior Density
process optimization
ram
sam
Seymour Gkisser
statistical model selection
xam
Product details
- ISBN 9780367449919
- Weight: 335g
- Dimensions: 138 x 216mm
- Publication Date: 03 Dec 2019
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
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The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive analyses for parametric analyses, but it also presents predictive analyses that have no real parametric analogues. It demonstrates that predictive inference can be a critical component of even strict parametric inference when dealing with interim analyses. This approach to predictive inference will be of interest to statisticians, psychologists, econometricians, and sociologists.
Geisser, Seymour
