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Bayesian Model Selection for Heteroskedastic Models
SSRN Electronic Journal, 2008It is well known that volatility asymmetry exists in financial markets. This paper reviews and investigates recently developed techniques for Bayesian estimation and model selection applied to a large group of modern asymmetric heteroskedastic models.
Chen, Cathy W.S. +2 more
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Bayesian Model Selection and Model Averaging
Journal of Mathematical Psychology, 2000This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.
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COSMOLOGICAL BAYESIAN MODEL SELECTION
Statistical Problems in Particle Physics, Astrophysics and Cosmology, 2006Bayesian model comparison can be used to decide whether the introduction of a new parameter is warranted by data. I focus on the Savage-Dickey density ratio as a method to compute the Bayes factor of nested models without carrying out a computationally demanding multi-dimensional integration.
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Bayesian Post-Model-Selection Estimation
IEEE Signal Processing Letters, 2021Estimation after model selection refers to the problem where the exact observation model is unknown and is assumed to belong to a set of candidate models. Thus, a data-based model-selection stage is performed prior to the parameter estimation stage, which affects the performance of the subsequent estimation.
Nadav Harel, Tirza Routtenberg
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Bayesian Model Selection for Pathological Data
2014The detection of abnormal intensities in brain images caused by the presence of pathologies is currently under great scrutiny. Selecting appropriate models for pathological data is of critical importance for an unbiased and biologically plausible model fit, which in itself enables a better understanding of the underlying data and biological processes ...
Carole H, Sudre +5 more
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Bayesian model selection in ARFIMA models
Expert Systems with Applications, 2010Various model selection criteria such as Akaike information criterion (AIC; Akaike, 1973), Bayesian information criterion (BIC; Akaike, 1979) and Hannan-Quinn criterion (HQC; Hannan, 1980) are used for model specification in autoregressive fractional integrated moving average (ARFIMA) models. Classical model selection criteria require to calculate both
Eǧrïoǧlu, Erol, Günay, Süleyman
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Bayesian selection of log‐linear models
Canadian Journal of Statistics, 1996AbstractA general methodology is presented for finding suitable Poisson log‐linear models with applications to multiway contingency tables. Mixtures of multivariate normal distributions are used to model prior opinion when a subset of the regression vector is believed to be nonzero.
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Item selection via Bayesian IRT models.
Statistics in medicine, 2014With reference to a questionnaire that aimed to assess the quality of life for dysarthric speakers, we investigate the usefulness of a model-based procedure for reducing the number of items. We propose a mixed cumulative logit model, which is known in the psychometrics literature as the graded response model: responses to different items are modelled ...
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