3 research outputs found
Reliable inference for complex models by discriminative composite likelihood estimation
Composite likelihood estimation has an important role in the analysis of
multivariate data for which the full likelihood function is intractable. An
important issue in composite likelihood inference is the choice of the weights
associated with lower-dimensional data sub-sets, since the presence of
incompatible sub-models can deteriorate the accuracy of the resulting
estimator. In this paper, we introduce a new approach for simultaneous
parameter estimation by tilting, or re-weighting, each sub-likelihood component
called discriminative composite likelihood estimation (D-McLE). The
data-adaptive weights maximize the composite likelihood function, subject to
moving a given distance from uniform weights; then, the resulting weights can
be used to rank lower-dimensional likelihoods in terms of their influence in
the composite likelihood function. Our analytical findings and numerical
examples support the stability of the resulting estimator compared to
estimators constructed using standard composition strategies based on uniform
weights. The properties of the new method are illustrated through simulated
data and real spatial data on multivariate precipitation extremes.Comment: 29 pages, 4 figure