On the robustness of maximum composite likelihood estimate

By Ximing Xu, Norman Reid. 2011

92 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/00cefdaff920c6262e674262c3db3acbbeef2671

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Fitting Vast Dimensional Time-Varying Covariance Models (2020, background): “Beyond computational gains, estimation based on lower dimensional marginal densities is also useful in a robustness sense: one is more likely to misspecify the more complicated joint density compared to the simpler univariate or bivariate marginal densities (Xu and Reid (2011)).”
Mapping extreme snowfalls in the French Alps using max‐stable processes (2013, methodology): “These authors also propose a practical inferential method for the fitting of MSPs to spatial data by maximization of a composite likelihood [Lindsay, 1988; Xu and Reid, 2011] since the full likelihood is out of reach.”
A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, methodology): “Specifically, under usual regularity assumptions (Molenberghs and Verbeke, 2005, page 191, Xu and Reid, 2011), the CML estimator of θ is consistent and asymptotically normal distributed with asymptotic mean θ and covariance matrix given by the inverse of Godambe’s (1960) sandwich information matrix…”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, background): “Xu and Reid (2011) provided several examples of cases where the CML is consistent while the full likelihood inference approach is not.”
Willingness to pay and attitudinal preferences of Indian consumers for electric vehicles (2021, unknown): “Under typical regularity conditions, the CML estimator is consistent and asymptotically Gaussian (Xu and Reid, 2011).”
A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling (2014, background): “…normally distributed (this is because of the unbiasedness of the CML score function, which is a linear combination of proper score functions associated with the marginal event probabilities forming the composite likelihood; for a formal proof, see Yi et al., 2011 and Xu and Reid, 2011).”