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August 2009 Likelihood Inference for Models with Unobservables: Another View
Youngjo Lee, John A. Nelder
Statist. Sci. 24(3): 255-269 (August 2009). DOI: 10.1214/09-STS277


There have been controversies among statisticians on (i) what to model and (ii) how to make inferences from models with unobservables. One such controversy concerns the difference between estimation methods for the marginal means not necessarily having a probabilistic basis and statistical models having unobservables with a probabilistic basis. Another concerns likelihood-based inference for statistical models with unobservables. This needs an extended-likelihood framework, and we show how one such extension, hierarchical likelihood, allows this to be done. Modeling of unobservables leads to rich classes of new probabilistic models from which likelihood-type inferences can be made naturally with hierarchical likelihood.


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Youngjo Lee. John A. Nelder. "Likelihood Inference for Models with Unobservables: Another View." Statist. Sci. 24 (3) 255 - 269, August 2009.


Published: August 2009
First available in Project Euclid: 31 March 2010

zbMATH: 1329.62337
MathSciNet: MR2757429
Digital Object Identifier: 10.1214/09-STS277

Keywords: extended likelihood , hierarchical generalized linear model , hierarchical likelihood , likelihood , random effects , unobservables

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.24 • No. 3 • August 2009
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