Abstract
July 2 - 9.30-10.20
A. Salvan, L. Pace
- The geometric structure of likelihood expansions in the presence of nuisance parameters
Stochastic expansions of likelihood quantities are usually derived through
ordinary Taylor expansions,
rearranging terms according to their asymptotic order.The most convenient
form for such expansions involves
the score function, the expected information, higher-order log-likelihood
derivatives and their
expectations. Expansions of this form are called expected/observed. If the
quantity expanded is a tensor
under a group of transformations on the parameter space, the entire
contribution of a given asymptotic
order to the expected/observed expansion will follow the same
transformation law. When there are no nuisance
parameters, explicit representations through appropriate tensors are
available. In this contribution, we analyse the geometric structure of
expected/observed likelihood expansions when nuisance parameters are
present. We outline the derivation of
likelihood quantities which behave as tensors under interest-respecting
reparameterisations. This allows
us to write the usual stochastic expansions of profile likelihood
quantities in an explicitly tensorial form.
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