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November 2008 Accurate Parametric Inference for Small Samples
Alessandra R. Brazzale, Anthony C. Davison
Statist. Sci. 23(4): 465-484 (November 2008). DOI: 10.1214/08-STS273


We outline how modern likelihood theory, which provides essentially exact inferences in a variety of parametric statistical problems, may routinely be applied in practice. Although the likelihood procedures are based on analytical asymptotic approximations, the focus of this paper is not on theory but on implementation and applications. Numerical illustrations are given for logistic regression, nonlinear models, and linear non-normal models, and we describe a sampling approach for the third of these classes. In the case of logistic regression, we argue that approximations are often more appropriate than ‘exact’ procedures, even when these exist.


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Alessandra R. Brazzale. Anthony C. Davison. "Accurate Parametric Inference for Small Samples." Statist. Sci. 23 (4) 465 - 484, November 2008.


Published: November 2008
First available in Project Euclid: 11 May 2009

zbMATH: 1329.62101
MathSciNet: MR2530546
Digital Object Identifier: 10.1214/08-STS273

Keywords: conditional inference , Heteroscedasticity , logistic regression , Lugannani–Rice formula , Markov chain Monte Carlo , nonlinear model , R , regression-scale model , saddlepoint approximation , Spline , statistical computing

Rights: Copyright © 2008 Institute of Mathematical Statistics


Vol.23 • No. 4 • November 2008
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