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Statistical Decision Theory and Bayesian Analysis
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TLDR
An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.Abstract:
1. Basic concepts 2. Utility and loss 3. Prior information and subjective probability 4. Bayesian analysis 5. Minimax analysis 6. Invariance 7. Preposterior and sequential analysis 8. Complete and essentially complete classes Appendices.read more
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Optimal selection of ensemble classifiers using measures of competence and diversity of base classifiers
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Bayesian Methods for Analyzing Structural Equation Models With Covariates, Interaction, and Quadratic Latent Variables
TL;DR: A Bayesian approach to analyze a general structural equation model that accommodates the general nonlinear terms of latent variables and covariates is introduced and produces a Bayesian estimate that has the same statistical optimal properties as a maximum likelihood estimate.
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Power prior distributions for generalized linear models
TL;DR: The power priors as mentioned in this paper are based on the notion of the availability of historical data and are of great potential use in this context, and demonstrate how to construct these priors and elicit their hyperparameters.
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Processing Information in Quantum Decision Theory
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Approaches for Empirical Bayes Confidence Intervals
TL;DR: In this article, a conditional bias correction method is proposed to correct the shortness of the EM intervals, since they do not attain the desired coverage probability in the EB sense defined by Morris (1983a, b).