ADJUSTING COMPOSITE LIKELIHOOD RATIO STATISTICS

By Luigi Pace, Alessandra Salvan, Nicola Sartori. 2009

93 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f2abc4028a002e4243d1a3b93fae4f77f10825be

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Citations in the corpus

Statistical Properties of Covariance Tapers (2013, methodology): “Inference based on approximated likelihoods that goes beyond using the Godambe information matrix is addressed in, for example, Chandler and Bate (2007) and Pace, Salvan, and Sartori (2011).”
The maximum approximate composite marginal likelihood (MACML) estimation of multinomial probit-based unordered response choice models (2011, unknown): “Pace et al. (2011) have recently proposed a way out, indicating that the following adjusted CLRT statistic, ADCLRT, may be considered to be asymptotically chi-squared distributed with d degrees of freedom: CLRTADCLRT × ′ ′ = − −− )()]([])([ )()]()][([)]([])([ 1 11 θSθHθS θSθHθGθHθS τττ τττττ ,…”
Minimum Scoring Rule Inference (2014, methodology): “Table 3 compares confidence regions for β based on the full likelihood ratio W (β), the Tsallis Wald statistic W S w (β) and the adjustments (23) and (24) of the Tsallis empirical score likelihood ratio statistic, and the Huber Wald statistic W w (β), when the central model is the normal one.”
AN OVERVIEW OF COMPOSITE LIKELIHOOD METHODS (2011, background): “If needed, we distinguish marginal, LMC, and conditional, LCC, composite likelihoods.”
A latent variable representation of count data models to accommodate spatial and temporal dependence: application to predicting crash frequency at intersections (2011, methodology): “Procedures similar to those available with the maximum likelihood approach are also available for model selection with the CML approach (see Varin and Vidoni, 2009, Pace et al., 2011 and Bhat, 2011).”
The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models (2014, unknown): “Pace et al. (2011) have recently proposed a way out, indicating that the following adjusted CLRT statistic, ADCLRT, may be considered to be asymptotically chi-squared distributed with d~ degrees of freedom: CLRTADCLRT × ′ ′ = − −− )()]([])([ )()]()][([)]([])([ 1 11 θSθHθS θSθHθGθHθS τττ τττττ…”