Supermodular comparison of dependence models and multivariate processes, with applications
Supermodular comparison of dependence models and multivariate processes, with applications
The supermodular order is a well-known tool to compare the intrinsic degree of dependence between random vectors or multivariate processes. In this note we describe a general framework for the supermodular comparisons of models incorporating individual and common factors. Examples are given on how to apply these models in comparing hitting times for multivariate processes of interest within risk analysis and reliability theory.
Positive dependence models, dependence orders, generalized Marshall–Olkin distributions, default models, series and parallel systems, multivariate distributions.
Positive dependence models, dependence orders, generalized Marshall–Olkin distributions, default models, series and parallel systems, multivariate distributions.
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