The likelihood for copula modeling appears when both the data and the copula representations are seen as being driven … by common uniform latent variables. This perspective facilitates Bayesian inference for prediction and copula selection. …

This article shows how to construct a likelihood for a general class of censoring problems. This likelihood is proven … normally distributed under regularity conditions. The method generalizes ordinary maximum likelihood estimation as well as …

The generalised likelihood uncertainty estimation (GLUE) approach was applied to assess the performance of a … average terms 5 m<Superscript>3</Superscript> s<Superscript> − 1</Superscript> before the Bayesian updating of the prediction ….3 and 1.5 m before and after the Bayesian updating of the prediction limits, respectively. Consequently, the results suggest …

Our research tested two predictions regarding how likelihood can have motivational effects as a function of how a … language of high likelihood (“80% A”) rather than low likelihood (“20% B”), i.e., high rather than low expressed likelihood … activity. We also predicted that strengthening engagement from the high (versus low) expressed likelihood of a future event …

First difference maximum likelihood (FDML) seems an attractive estimation methodology in dynamic panel data modeling … finite sample performance and asymptotics. FDML uses the Gaussian likelihood function for first differenced data and … parameter estimation is based on the whole domain over which the log-likelihood is defined. However, extending the domain of the …

averages and are smooth functions of a parameter θ. This includes log likelihood, quasi-log likelihood, and least squares …