A New Generalisation of Sam-Solai's Multivariate Additive Beta Distribution of Kind-2 of Type-A
Sam Jayakumar, A. Solairaju, S Dawood Ali
Abstract
Sam Jayakumar, A. Solairaju, S Dawood Ali
Abstract
This paper proposed a new generalization of bounded continuous multivariate symmetric probability distributions. More specifically the authors visualizes a new generalization of Sam-Solai’s multivariate additive Beta distribution of Kind-2 of Type-A from the uni-variate two parameter Beta distribution of Kind-1. Further,we find its marginal, multivariate conditional distributions, multivariate generating functions, multivariate survival, hazard functions and also discussed it’s special cases. The special cases includes the transformation of Sam-Solai’s multivariate additive Beta distribution of Kind 2 of Type-A into multivariate additive Beta distribution of Kind-1 of Type-A, Multivariate F-distribution of Kind-1, Multivariate standard Logistic-Beta distribution of Kind-1. Moreover, it is found that the bivariate correlation between two Beta random variables purely depends on the shape parameter and we simulated and established selected standard bivariate Beta correlation bounds from 10,000 different combinations of values for shape parameter.
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This paper proposed a new generalization of bounded continuous multivariate symmetric probability distributions. More specifically the authors visualizes a new generalization of Sam-Solai’s multivariate additive Beta distribution of Kind-2 of Type-A from the uni-variate two parameter Beta distribution of Kind-1. Further,we find its marginal, multivariate conditional distributions, multivariate generating functions, multivariate survival, hazard functions and also discussed it’s special cases. The special cases includes the transformation of Sam-Solai’s multivariate additive Beta distribution of Kind 2 of Type-A into multivariate additive Beta distribution of Kind-1 of Type-A, Multivariate F-distribution of Kind-1, Multivariate standard Logistic-Beta distribution of Kind-1. Moreover, it is found that the bivariate correlation between two Beta random variables purely depends on the shape parameter and we simulated and established selected standard bivariate Beta correlation bounds from 10,000 different combinations of values for shape parameter.
Key concepts: Multivariate stable distribution, Multivariate statistics, Mathematics, Normal-Wishart distribution, Multivariate t-distribution, Matrix t-distribution, Multivariate normal distribution, Beta distribution