# Model selection

## Papers overview

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Highly Cited

2010

Highly Cited

2010

- 2010

We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based onâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- NeuroImage
- 2009

Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of competing hypotheses about theâ€¦Â (More)

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Highly Cited

2009

Highly Cited

2009

- Journal of the Royal Society, Interface
- 2009

Approximate Bayesian computation (ABC) methods can be used to evaluate posterior distributions without having to calculateâ€¦Â (More)

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Highly Cited

2008

Highly Cited

2008

- 2008

The ordinary Bayesian information criterion is too liberal for model selection when the model space is large. In this paper, weâ€¦Â (More)

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Highly Cited

2007

Highly Cited

2007

- 2007

We propose penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model. The methods leadâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- 2006

We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arisesâ€¦Â (More)

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Highly Cited

2006

Highly Cited

2006

- Journal of Machine Learning Research
- 2006

Sparsity or parsimony of statistical models is crucial for their proper interpretations, as in sciences and social sciencesâ€¦Â (More)

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Highly Cited

1997

Highly Cited

1997

- 1997

We argue that model selection uncertainty should be fully incorporated into statistical inference whenever estimation isâ€¦Â (More)

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Highly Cited

1993

Highly Cited

1993

- 1993

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofâ€¦Â (More)

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Highly Cited

1989

Highly Cited

1989

- 1989

A bias correction to the Akaike information criterion, AIC, is derived for regression and autoregressive time series models. Theâ€¦Â (More)

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