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On the adequacy of variational lower bound functions for likelihood-based inference in Markovian models with missing values
Ist Teil von
Journal of the Royal Statistical Society. Series B, Statistical methodology, 2002-01, Vol.64 (3), p.549-564
Ort / Verlag
Cowley Road, Oxford, UK: Blackwell Publishers
Erscheinungsjahr
2002
Link zum Volltext
Quelle
EBSCOhost Business Source Ultimate
Beschreibungen/Notizen
Variational methods have been proposed for obtaining deterministic lower bounds for log-likelihoods within missing data problems, but with little formal justification or investigation of the worth of the lower bound surfaces as tools for inference. We provide, within a general Markovian context, sufficient conditions under which estimators from the variational approximations are asymptotically equivalent to maximum likelihood estimators, and we show empirically, for the simple example of a first-order autoregressive model with missing values, that the lower bound surface can be very similar in shape to the true log-likelihood in non-asymptotic situations.