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Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
Spatial econometrics relies on spatial weights matrix to specify the cross sectional depen-dence, which might not be unique. This paper proposes a model selection procedure to choose an optimal weight...
Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to ...
The joint modeling of longitudinal and time-to-event data is an active area of statistics research that has received a lot of attention in the recent years. More recently, a new and attractive applica...
A major challenge to the statistical analysis of microarray data is the small number of samples limited by both cost and sample availability compared to the large number of genes, now soaring into t...
The problem of evaluating the goodness of the predictive distributions developed by the Bayesian model averaging approach is investigated. Considering the maximization of the posterior mean of the exp...
Flexibly modeling the response variance in regression is important for efficient parameter estimation, correct inference, and for understanding the sources of variability in the response. Our articl...
The finite-sample as well as the asymptotic distribution of Leung and Barron’s (2006) model averaging estimator are derived in the context of a linear regression model. An impossibility result regar...

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