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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Additive modelAkaike information criterionAlgorithmic information theoryAll models are wrong
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Papers overview

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Highly Cited
2010
Highly Cited
2010
High-dimensional Ising model selection using â„“1-regularized logistic regression
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 for group studies
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 scheme for parameter inference and model selection in dynamical systems.
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
Extended Bayesian information criteria for model selection with large model spaces
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
Model selection and estimation in the Gaussian graphical model
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
Model selection and estimation in regression with grouped variables
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
On Model Selection Consistency of Lasso
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
Model selection: An integral part of inference
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
Linear Model Selection by Cross-validation
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
Regression and time series model selection in small samples
A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. The… (More)
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