Results 151 to 160 of about 152,582 (189)
An overview of composite likelihood methods [PDF]
, 2011 A survey of recent developments in the theory and application of composite likelihood is provided, building on the review paper of Varin (2008). A range of application areas, including geostatistics, spatial extremes, and space-time models, as well as clustered and longitudinal data and time series are considered.
VARIN, Cristiano, REID N., FIRTH D.
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Towards a unification of second-order theory for likelihood and marginal composite likelihood [PDF]
, 2015 An adjustment for marginal composite likelihoods is derived to match the second-order theory of the likelihood when inference is for a vector-valued parameter in the absence of nuisance components. The adjustment overcomes the failure of Bartlett identities for marginal composite likelihoods and leads to a Bartlett-correctable marginal composite ...
Lunardon N.
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Canadian Journal of Statistics, 2014
AbstractComposite likelihood appears to be an appealing alternative to the full likelihood when the latter is too complex. For a given model, there may be different ways to formulate a composite likelihood, for example, pairwise marginal or conditional likelihood.
KENNE PAGUI, EULOGE CLOVIS +2 more
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AbstractComposite likelihood appears to be an appealing alternative to the full likelihood when the latter is too complex. For a given model, there may be different ways to formulate a composite likelihood, for example, pairwise marginal or conditional likelihood.
KENNE PAGUI, EULOGE CLOVIS +2 more
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On composite marginal likelihoods
AStA Advances in Statistical Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Likelihood and Composite Hypotheses [Comment on “A Likelihood Paradigm for Clinical Trials”]
Journal of Statistical Theory and Practice, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Gaussian Process Regression With Maximizing the Composite Conditional Likelihood
IEEE Transactions on Instrumentation and Measurement, 2021Gaussian process regression (GPR) has an outstanding nonlinear fitting ability, and its uncertainty predictions can deliver the confidence level of the estimations, which is well adapted to deal with complex industrial processes. However, disturbances and noises in outputs might lead to mispredictions for new samples.
Haojie Huang 0002 +4 more
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A composite likelihood approach to predict the sex of the baby
Statistical Methods in Medical Research, 2017Couples with diseases associated with the sexual chromosomes, as well as families in countries where the desire for a male is extreme, are interested in influencing the sex of the baby. We propose an original composite likelihood approach to analyse the relation between sex of the newborn and timing of the intercourse which leads to conception ...
Tiberi, Simone +2 more
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BAYESIAN COMPOSITE MARGINAL LIKELIHOODS
2011This paper proposes and discusses the use of composite marginal like- lihoods for Bayesian inference. This approach allows one to deal with complex statistical models in the Bayesian framework, when the full likelihood - and thus the full posterior distribution - is impractical to compute or even analytically un- known.
PAULI, FRANCESCO, Racugno W., Ventura L.
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