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Predictive Model Selection
 Journal of the Royal Statistical Society, Ser. B
, 1995
"... this article we propose three criteria that can be used to address model selection. These emphasize observables rather than parameters and are based on a certain Bayesian predictive density. They have a unifying basis that is simple and interpretable,are free of asymptotic de#nitions,and allow the i ..."
Abstract

Cited by 62 (4 self)
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this article we propose three criteria that can be used to address model selection. These emphasize observables rather than parameters and are based on a certain Bayesian predictive density. They have a unifying basis that is simple and interpretable,are free of asymptotic de#nitions,and allow the incorporation of prior information. Moreover,two of these criteria are readily calibrated.
Numerical linear algebra in the integrity theory of the Global Positioning System �
"... The Global Positioning System (GPS) is a satellite basednavigation system. Since safety is the main concern for aircraft navigation, various means of monitoring the integrity (certainty of position) have been developed. This is an important area of research in the GPS community. In the following, it ..."
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The Global Positioning System (GPS) is a satellite basednavigation system. Since safety is the main concern for aircraft navigation, various means of monitoring the integrity (certainty of position) have been developed. This is an important area of research in the GPS community. In the following, it will be shown how some numerical linear algebra techniques can be applied to this interesting application. A typical model is presented. A uniform approach to derive the statistics for fault detection and isolation by orthogonal transformations is given. It is shown that the diagonal elements! 2 ii of the orthogonal projection matrix onto the residual space are fundamental to the theory and understanding of integrity.!ii can, for example, have a drastic e ect on integrity when they are small. The sensitivity of relatedproblems in this area are
Bayesian Unmasking in Linear Models
 CORE DISCUSSION PAPER 9619, UNIVERSIT# CATHOLIQUE DE LOUVAIN
"... We propose a Bayesian procedure for multiple outlier detection in linear models whichavoids the masking problem. The posterior probabilities of each data point being an outlier are estimated by using an adaptive learning Gibbs sampling method. The idea is to modify the initial conditions of the Gibb ..."
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We propose a Bayesian procedure for multiple outlier detection in linear models whichavoids the masking problem. The posterior probabilities of each data point being an outlier are estimated by using an adaptive learning Gibbs sampling method. The idea is to modify the initial conditions of the Gibbs sampler in order to visit the posterior distribution space in a reasonable number of iterations. To find an appropriate vector of initial values we consider the information extracted from the eigenstructure of the covariance matrix of a vector of latent variables. These variables are introduced in the model to capture the heterogeneity in the data. This procedure also overcomes the false convergence of the Gibbs sampling in problems with strong masking. Our proposal is illustrated with some of the examples most frequently used in the literature.