Statistical Science

Likelihood Inference for Models with Unobservables: Another View

Youngjo Lee and John A. Nelder

Full-text: Open access

Abstract

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.

Article information

Source
Statist. Sci., Volume 24, Number 3 (2009), 255-269.

Dates
First available in Project Euclid: 31 March 2010

Permanent link to this document
https://projecteuclid.org/euclid.ss/1270041252

Digital Object Identifier
doi:10.1214/09-STS277

Mathematical Reviews number (MathSciNet)
MR2757429

Zentralblatt MATH identifier
1329.62337

Keywords
Hierarchical generalized linear model unobservables random effects likelihood extended likelihood hierarchical likelihood

Citation

Lee, Youngjo; Nelder, John A. Likelihood Inference for Models with Unobservables: Another View. Statist. Sci. 24 (2009), no. 3, 255--269. doi:10.1214/09-STS277. https://projecteuclid.org/euclid.ss/1270041252


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