Quantifying dependence between extreme values is a central problem in many theoretical and applied studies. The main distinction is between asymptotically independent and asymptotically dependent extremes, with important theoretical examples of these general limiting classes being the extremal behaviour of a bivariate Normal distribution, for asymptotic independence, and of the bivariate t dist...

Study of the bivariate normal distribution raises the full range of issues involving objective Bayesian inference, including the different types of objective priors (e.g., Jeffreys, invariant, reference, matching), the different modes of inference (e.g., Bayesian, frequentist, fiducial), and the criteria involved in deciding on optimal objective priors (e.g., ease of computation, frequentist pe...

Birnbaum and Saunders introduced in 1969 a two-parameter lifetime distribution which has been used quite successfully to model a wide variety of univariate positively skewed data. Diaz-Garcia and Leiva-Sanchez [9] proposed a generalized BirnbaumSaunders distribution by using an elliptically symmetric distribution in place of the normal distribution. Recently, Kundu et al. [17] introduced a biva...

In certain circumstances inference based on the likelihood function can be hindered by, for example, computational complexity; new applications of directional statistics to bioinformatics problems give many obvious examples. In such cases it is necessary to seek an alternative method of estimation. Two pseudolikelihoods, each based on conditional distributions, are assessed in terms of their ef...