Model selection

Known as: Model comparison 
Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of… (More)
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Highly Cited
2010
Highly Cited
2010
We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on… (More)
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Highly Cited
2009
Highly Cited
2009
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about the… (More)
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Highly Cited
2009
Highly Cited
2009
Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculate… (More)
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Highly Cited
2008
Highly Cited
2008
The ordinary Bayesian information criterion is too liberal for model selection when the model space is large. In this paper, we… (More)
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Highly Cited
2007
Highly Cited
2007
We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods lead… (More)
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Highly Cited
2006
Highly Cited
2006
We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arises… (More)
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Highly Cited
2006
Highly Cited
2006
Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciences… (More)
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Highly Cited
1997
Highly Cited
1997
We argue that model selection uncertainty should be fully incorporated into statistical inference whenever estimation is… (More)
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Highly Cited
1993
Highly Cited
1993
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of… (More)
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Highly Cited
1989
Highly Cited
1989
A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The… (More)
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