Modern advances in likelihood ratio inference

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• Moulinath Banerjee (University of Michigan, Ann Arbor, USA)
• Wednesday 30 May 2012, 16:30-17:30
• MR2, CMS.

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Since Wilks’ seminal 1938 paper which demonstrated the convergence of the likelihood ratio statistic in a parametric model to a limiting chi-squared distribution, likelihood ratios have been used extensively for inferential purposes, not least owing to the fact the they are asymptotically pivotal. In this presentation, I will highlight some of the modern developments in the theory of likelihood ratio inference where asymptotic pivotality continues to be preserved, focusing mainly on likelihood ratio inference on differentiable functionals in semiparametric models and pointwise likelihood ratios in shape constrained estimation.

This talk is part of the Statistics Reading Group series.

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