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Assessment of Locally Influential Observations in Bayesian Models
Case sensitivity Kullback-Leibler divergence influence predictive density posterior density quasi-posterior
2009/9/22
In models with conditionally independent observations, it is shown
that the posterior variance of the log-likelihood from observation i is a measure
of that observation local inuence. This result is...
Geographically Assisted Elicitation of Expert Opinion for Regression Models
elicitation expert opinion regression
2009/9/22
One of the perceived strengths of Bayesian modelling is the ability to
include prior information. Although objective or noninformative priors might be
preferred in some situations, in many other app...
Dynamic Matrix-Variate Graphical Models
Bayesian Forecasting Dynamic Linear Models Gaussian Graphical Models Graphical Model Uncertainty Hyper-Inverse Wishart Distribution
2009/9/22
This paper introduces a novel class of Bayesian models for multivariate
time series analysis based on a synthesis of dynamic linear models and graphical
models. The synthesis uses sparse graphical m...
On the bootstrapping heteroscedastic regression models
Bootstrap heteroscedastic regression ordinary least squares estimates
2009/9/21
The distributjons of deviations of point estimators for
parameters of iterest are essential in the evvaluation of the eficiency of
point estimators. The bootstrap method suggested by B. Efron is on...
Multi-Scale and Hidden Resolution Time Series Models
Autoregressive models Bayesian inference Combination of multiresolution information Jerey rule of conditioning
2009/9/21
We introduce a class of multi-scale models for time series. The novel framework couples standard linear models at dierent levels of resolution via stochastic links across scales. Jerey rule of conditi...
Loss Function Based Ranking in Two-Stage, Hierarchical Models
percentiling Bayesian models decision theory operating characteristic
2009/9/21
Performance evaluations of health services providers burgeons. Simi-
larly, analyzing spatially related health information, ranking teachers and schools,
and identification of differentially express...
Variational Bayesian Learning of Directed Graphical Models with Hidden Variables
Approximate Bayesian Inference Bayes Factors Directed Acyclic Graphs EM Algorithm Graphical Models Markov Chain Monte Carlo
2009/9/21
A key problem in statistics and machine learning is inferring suitable
structure of a model given some observed data. A Bayesian approach to model
comparison makes use of the marginal likelihood of ...
Almost sure properties of weighted vectorial martingales transforms with applications to prediction for linear regression models
least squares estimators cumulative prediction and estimation Linear regression models
2009/9/21
We establish new almost sure properties for powers of
weighted martingale transbrms. It allows us to deduce usefuI asymptotic
results for cumulative prediction and estimation errors associated
with...
Deviance Information Criteria for Missing Data Models
completion deviance DIC EM algorithm MAP model comparison mixture model random effect model
2009/9/21
The deviance information criterion(DIC)introduced by Spiegelhalter et al.
(2002) for model assessment and model comparison is directly inspired by linear
and generalised linear models, but it is ope...
The Relationship Between the Power Prior and Hierarchical Models
Generalized linear model hierarchical model historical data power prior prior elicitation random eects model
2009/9/21
The power prior has emerged as a useful informative prior for the incorpora-
tion of historical data in a Bayesian analysis. Viewing hierarchical modeling as
the gol standard for combining informati...
A Default Conjugate Prior for Variance Components in Generalized Linear Mixed Models(Comment on Article by Browne and Draper)
Choice of prior hierarchical models noninformative priors random effects
2009/9/21
For a scalar random-eect variance, Browne and Draper (2005) have found that the uniform prior works well. It would be valuable to know more about the vector case, in which a second-stage prior on the ...
Prior distributions for variance parameters in hierarchical models(Comment on Article by Browne and Draper)
Bayesian inference conditional conjugacy folded-noncentral-t distribution hierarchical model multilevel mode weakly informative prior distribution
2009/9/21
Various noninformative prior distributions have been suggested for
scale parameters in hierarchical models. We construct a new folded-noncentral-t
family of conditionally conjugate priors for hierar...
A comparison of Bayesian and likelihood-based methods for fitting multilevel models
Adaptive MCMC bias calibration diuse priors hierarchical modeling MQL mixed models PQL RIGLS random-eects logistic regression
2009/9/21
We use simulation studies, whose design is realistic for educational
and medical research (as well as other elds of inquiry), to compare Bayesian and
likelihood-based methods for tting variance-comp...
On ARMA(1,q) models with bounded and periodically correlated solutions
Periodically correlated ARMA model periodic coefficients
2009/9/21
In this paper, motivated by [2], we derive necessary
and suficient conditions for bounded and periodically correlated solutions
to the system of equations described by ARMA(1, q) model.
Bayesian auxiliary variable models for binary and multinomial regression
Auxiliary variables Bayesianb inary and multinomial regression Model averaging Scale mixture of normals
2009/9/21
In this paper we discuss auxiliary variable approaches to Bayesia
binary and multinomial regression. These approaches are ideally suited to au
tomated Markov chain Monte Carlo simulation. In the rst...