Multivariate hazard rate orders
Journal of Multivariate Analysis
Dependence, Dispersiveness, And Multivariate Hazard Rate Ordering
Probability in the Engineering and Informational Sciences
Multivariate dynamic information
Journal of Multivariate Analysis
Characterizations of Arnold and Strauss' and related bivariate exponential models
Journal of Multivariate Analysis
Multivariate maximum entropy identification, transformation, and dependence
Journal of Multivariate Analysis
A class of models for uncorrelated random variables
Journal of Multivariate Analysis
Hi-index | 0.00 |
This article develops information optimal models for the joint distribution based on partial information about the survival function or hazard gradient in terms of inequalities. In the class of all distributions that satisfy the partial information, the optimal model is characterized by well-known information criteria. General results relate these information criteria with the upper orthant and the hazard gradient orderings. Applications include information characterizations of the bivariate Farlie–Gumbel–Morgenstern, bivariate Gumbel, and bivariate generalized Gumbel, for which no other information characterization are available. The generalized bivariate Gumbel model is obtained from partial information about the survival function and hazard gradient in terms of marginal hazard rates. Other examples include dynamic information characterizations of the bivariate Lomax and generalized bivariate Gumbel models having marginals that are transformations of exponential such as Pareto, Weibull, and extreme value. Mixtures of bivariate Gumbel and generalized Gumbel are obtained from partial information given in terms of mixtures of the marginal hazard rates.