We consider a discrete-time risk model,in which insurance risks and financial risks jointly follow a multivariate Farlie-Gumbel-Morgenstern distribution,and the insurance risks are regularly varying tailed.Explicit asymptotic formulae are obtained for finite-time and infinite-time ruin probabilities.Some numerical results are also presented to illustrate the accuracy of our asymptotic formulae.
Modeling the mean and covariance simultaneously is a common strategy to efciently estimate the mean parameters when applying generalized estimating equation techniques to longitudinal data.In this article,using generalized estimation equation techniques,we propose a new kind of regression models for parameterizing covariance structures.Using a novel Cholesky factor,the entries in this decomposition have moving average and log innovation interpretation and are modeled as linear functions of covariates.The resulting estimators for the regression coefcients in both the mean and the covariance are shown to be consistent and asymptotically normally distributed.Simulation studies and a real data analysis show that the proposed approach yields highly efcient estimators for the parameters in the mean,and provides parsimonious estimation for the covariance structure.
