Marginal model

Known as: Marginal 
In statistics, marginal models (Heagerty & Zeger, 2000) are a technique for obtaining regression estimates in multilevel modeling, also called… (More)
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Highly Cited
2013
Highly Cited
2013
  • Justin Domke
  • IEEE Transactions on Pattern Analysis and Machine…
  • 2013
Likelihood-based learning of graphical models faces challenges of computational complexity and robustness to model… (More)
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Highly Cited
2009
Highly Cited
2009
We introduce a powerful and flexible MCMC algorithm for stochas-tic simulation. The method builds on a pseudo-marginal method… (More)
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Highly Cited
2008
Highly Cited
2008
The method of inverse probability weighting (henceforth, weighting) can be used to adjust for measured confounding and selection… (More)
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Highly Cited
2007
Highly Cited
2007
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor… (More)
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Highly Cited
2004
Highly Cited
2004
  • Kosuke Imaia, David A. van Dykb
  • 2004
We introduce a set of new Markov chain Monte Carlo algorithms for Bayesian analysis of the multinomial probit model. Our Bayesian… (More)
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Highly Cited
2003
Highly Cited
2003
The ‘sparse Bayesian’ modelling approach, as exemplified by the ‘relevance vector machine’, enables sparse classification and… (More)
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Highly Cited
2001
Highly Cited
2001
This article provides a framework for estimating the marginal likelihood for the purpose of Bayesian model comparisons . The… (More)
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Highly Cited
2000
Highly Cited
2000
Robins (1993, 1994, 1997, 1998ab) has developed a set of causal or counterfactual models, the structural nested models (SNMs… (More)
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Highly Cited
1999
Highly Cited
1999
The subject-specific data from a longitudinal study consist of a string of numbers. These numbers represent a series of empirical… (More)
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Highly Cited
1995
Highly Cited
1995
Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire… (More)
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