Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...

There is hardly any literature on modelling nonlinear dynamic relations involving nonnormal time series data. This is a serious lacuna because nonnormal data are far more abundant than normal ones, ...

An algorithm for the computation of a maximum likelihood estimate of the offspring distribution in a Bienaymé-Galton-Watson branching process is presented. Although the offspring distribution in ...

In certain multivariate problems the full probability density has an awkward normalizing constant, but the conditional and/or marginal distributions may be much more tractable. In this paper we ...

Maximum likelihood estimation of the parameters of a statistical model involves maximizing the likelihood or, equivalently, the log likelihood with respect to the parameters. The parameter values at ...

The challenge of using small sample sizes for operational risk capital models fitted via maximum likelihood estimation is well recognized, yet the literature generally provides warning examples rather ...