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Improved likelihood inference for discrete data

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Discrete data, particularly count and contingency table data, are typically analysed by using methods that are accurate to first order, such as normal approximations for maximum likelihood estimators. By contrast continuous data can quite generally be analysed by using third-order procedures, with major improvements in accuracy and with intrinsic separation of information concerning parameter components. The paper extends these higher order results to discrete data, yielding a methodology that is widely applicable and accurate to second order. The extension can be described in terms of an approximating exponential model that is expressed in terms of a score variable. The development is outlined and the flexibility of the approach is illustrated by examples.

Keywords: Binary regression; Categorical data; Conditional inference; Contingency tables; Likelihood; Negative binomial; Non-canonical link function

Document Type: Research Article


Affiliations: 1: Ecole Polytechnique Fédérale de Lausanne, Switzerland 2: University of Toronto, Canada

Publication date: 2006-06-01

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