A note on pseudolikelihood constructed from marginal densities

By D. R. Cox, Nancy Reid. 2004

471 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c305e124fac1ea643c171ec6f034c7d1d97722aa

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Citations in the corpus

ADJUSTING COMPOSITE LIKELIHOOD RATIO STATISTICS (2009, background): “The special case with µ = 0 and σ2 = 1, where θ = ρ, has been treated in detail by Cox and Reid (2004).”
A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling (2014, methodology): “The properties of the CML estimator may be derived using the theory of estimating equations (see Cox and Reid, 2004, Yi et al., 2011).”
The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models (2011, methodology): “, 1 ] [ ) ( − = ) (θ G θ VCML ) and the ML estimator ( 1 )] ( [ − θ H ) is positive semi-definite (see Cox and Reid, 2004; Zhao and Joe, 2005). 6 Specifically, McFadden and Train (2000) indicate, in their use of independent number of random draws across observations, that the difference between the ”
Joint composite estimating functions in spatiotemporal models (2012, background): “According to the CL literature (Reid and Cox, 2004; Varin et al., 2011), a common version of composite estimating functions is”
Fitting Vast Dimensional Time-Varying Covariance Models (2020, methodology): “…function using marginal univariate likelihoods (this type of marginal analysis has appeared before outside the time-series literature, e.g., Besag (1974) in spatial processes, LeCessie and van Houwelingen (1994) and Kuk and Nott (2000) for correlated binary data, and deLeon (2005) on grouped data).”
Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods (2009, methodology): “Approaches of this kind have been proposed in various papers in the statistical literature before, for example the pseudo-likelihood approach of Besag (Besag, 1975) and the treatments of composite likelihoods in Lindsay (1998) and Cox and Reid (2004) among others.”
ISSUES AND STRATEGIES IN THE SELECTION OF COMPOSITE LIKELIHOODS (2011, background): “discussed by Besag (1974), Lindsay (1988), and Cox and Reid (2004).”
Bayesian composite marginal likelihoods (2011, methodology): “An important contribution here is given by approximate likelihoods based on the composition of marginal distributions (Varin (2008)), particularly for bivariate marginal distributions (Cox and Reid (2004)), termed composite marginal likelihoods .”
A comparison of the maximum simulated likelihood and composite marginal likelihood estimation approaches in the context of the multivariate ordered-response model (2010, methodology): “…level mqi through threshold bounds (see McKelvey and Zavoina, 1975): 2 A handful of studies (see Hjort and Varin, 2008; Mardia et al., 2009; Cox and Reid, 2004) have also theoretically examined the limiting normality properties…”
A pairwise likelihood approach to generalized linear models with crossed random effects (2005, methodology): “with H(q) 1⁄4 Ef @U2(q; Y)=@qg and A(q) 1⁄4 EfU2(q; Y) U2(q; Y)g; see Cox and Reid (2004). The matrix H(q) can be estimated by the Hessian of minus ‘2(q; y) evaluated at q̂2(y).”
Composite likelihood estimation for the Brown–Resnick process (2013, background): “…= n∑ i=1 ∑ j1< j2 log f (zi, j1 , zi, j2; θ), 3(θ) = n∑ i=1 ∑ j1< j2< j3 log f (zi, j1 , zi, j2 , zi, j3; θ), and the corresponding maximum likelihood estimators θ̂2 and θ̂3, which are consistent and asymptotically Gaussian as n increases (Lindsay, 1988; Cox & Reid, 2004; Varin et al., 2011).”
Statistical inference for max‐stable processes in space and time (2012, methodology): “These methods go back to Besag (1974), and an extensive literature is available dealing with applications and properties of the estimates; see for example Cox and Reid (2004), Lindsay (1988), Varin (2007) or Varin and Vidoni (2005).”
Statistical Modeling of Spatial Extremes (2012, methodology): “eters θ of the model may be identified from the pairwise marginal densities, they may be estimated by maximizing a composite log likelihood function of the form (Lindsay (1988); Cox and Reid (2004); Varin (2008))”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, unknown): “…search by multiple testing implicitly assumes that for all cliques (i.e. fully connected subset of nodes) of size three and more the underlying joint distribution is well approximated by the product of the bivariate densities associated with the respective undirected edges (Cox and Reid, 2004 ).”
COMMENTS ON PAIRWISE LIKELIHOOD IN TIME SERIES MODELS (2011, background): “A general discussion of pairwise likelihood can be found in Cox and Reid (2004).”
On composite likelihood estimation of a multivariate INAR(1) model (2013, background): “Cox and Reid (2004) also note that composite likelihood estimation is typically fine for data that may be decomposed in independent or pseudo–independent replicates and discourage its use with long memory processes.”