Parameter Orthogonality and Approximate Conditional Inference

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ID: 294857
1987
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Ranked #115 of 145 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

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Abstract
SUMMARY We consider inference for a scalar parameter Ψ in the presence of one or more nuisance parameters. The nuisance parameters are required to be orthogonal to the parameter of interest, and the construction and interpretation of orthogonalized parameters is discussed in some detail. For purposes of inference we propose a likelihood ratio statistic constructed from the conditional distribution of the observations, given maximum likelihood estimates for the nuisance parameters. We consider to what extent this is preferable to the profile likelihood ratio statistic in which the likelihood function is maximized over the nuisance parameters. There are close connections to the modified profile likelihood of Barndorff-Nielsen (1983). The normal transformation model of Box and Cox (1964) is discussed as an illustration.
Reference Key
openalex_W1959492 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors D. R. Cox, Nancy Reid
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 1987
DOI
10.1111/j.2517-6161.1987.tb01422.x
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