A NEW GENERALISATION OF SAM-SOLAI'S MULTIVARIATE ADDITIVE EXPONENTIAL DISTRIBUTION*
G. S. David Sam Jayakumar, A. Solairaju, A. Sulthan
Abstract
G. S. David Sam Jayakumar, A. Solairaju, A. Sulthan
Abstract
T his 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 Exponential distribution from the uni-variate exponential distribution.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 exponential distribution into Multivariate Inverse exponential distribution, Multivariate Weibull distribution, Multivariate Power law distribution, Multivariate chi-square distribution with two d.f, Multivariate Rayleigh distribution, Multivariate Pareto distribution, Multivariate logistic distribution, Multivariate Generalized extreme value distribution and Multivariate Benktander weibull distribution. Moreover, the bivariate correlation between any two exponential random variables found to be -0.25 and it is independent from the Co-variance. Similarly, we simulated and established a symmetric matrix of Co-variances based on different combinations of values for parameters.
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T his 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 Exponential distribution from the uni-variate exponential distribution.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 exponential distribution into Multivariate Inverse exponential distribution, Multivariate Weibull distribution, Multivariate Power law distribution, Multivariate chi-square distribution with two d.f, Multivariate Rayleigh distribution, Multivariate Pareto distribution, Multivariate logistic distribution, Multivariate Generalized extreme value distribution and Multivariate Benktander weibull distribution. Moreover, the bivariate correlation between any two exponential random variables found to be -0.25 and it is independent from the Co-variance. Similarly, we simulated and established a symmetric matrix of Co-variances based on different combinations of values for parameters.
Key concepts: Multivariate stable distribution, Normal-Wishart distribution, Inverse-Wishart distribution, Mathematics, Matrix t-distribution, Wishart distribution, Multivariate statistics, Multivariate t-distribution