2012Research journal of pure algebraRequires access

• A NEW GENERALISATION OF SAM-SOLAI’S MULTIVARIATE ADDITIVE NAGAKAMI-M DISTRIBUTION

G. S. David Sam Jayakumar, A. Solairaju, A. Sulthan

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Abstract

This paper proposed a new generalization of bounded Continuous multivariate symmetric probability distributions. In this paper, we visualize a new generalization of Sam-Solai’s Multivariate additive Nagakami-m distribution from the univariate two parameter Nagakami-m distributions. Further, we find its Marginal, Multivariate Conditional distributions, Multivariate Generating functions, Multivariate survival, hazard functions and also discussed its special cases. The special cases includes the transformation of Sam-Solai’s Multivariate additive Nagakami-m distribution into Multivariate additive half normal distribution, Multivariate additive chi-distribution, Multivariate additive Inverse Nagakami-m distribution, Multivariate additive log-Nagakami-m distribution, Multivariate additive Extreme value Nagakami-m distribution, Multivariate additive Gamma distribution, Multivariate additive Chi-square distribution and Multivariate additive Erlang-k distribution. Moreover, it is found that the bivariate correlation between two Nagakami random variables purely depends on the shape parameter and we simulated and established selected standard bivariate Nagakami correlation bounds from 2500 different combinations of values for shape parameter.

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What this paper is about

This paper proposed a new generalization of bounded Continuous multivariate symmetric probability distributions. In this paper, we visualize a new generalization of Sam-Solai’s Multivariate additive Nagakami-m distribution from the univariate two parameter Nagakami-m distributions. Further, we find its Marginal, Multivariate Conditional distributions, Multivariate Generating functions, Multivariate survival, hazard functions and also discussed its special cases. The special cases includes the transformation of Sam-Solai’s Multivariate additive Nagakami-m distribution into Multivariate additive half normal distribution, Multivariate additive chi-distribution, Multivariate additive Inverse Nagakami-m distribution, Multivariate additive log-Nagakami-m distribution, Multivariate additive Extreme value Nagakami-m distribution, Multivariate additive Gamma distribution, Multivariate additive Chi-square distribution and Multivariate additive Erlang-k distribution. Moreover, it is found that the bivariate correlation between two Nagakami random variables purely depends on the shape parameter and we simulated and established selected standard bivariate Nagakami correlation bounds from 2500 different combinations of values for shape parameter.

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Available abstract

This paper proposed a new generalization of bounded Continuous multivariate symmetric probability distributions. In this paper, we visualize a new generalization of Sam-Solai’s Multivariate additive Nagakami-m distribution from the univariate two parameter Nagakami-m distributions. Further, we find its Marginal, Multivariate Conditional distributions, Multivariate Generating functions, Multivariate survival, hazard functions and also discussed its special cases. The special cases includes the transformation of Sam-Solai’s Multivariate additive Nagakami-m distribution into Multivariate additive half normal distribution, Multivariate additive chi-distribution, Multivariate additive Inverse Nagakami-m distribution, Multivariate additive log-Nagakami-m distribution, Multivariate additive Extreme value Nagakami-m distribution, Multivariate additive Gamma distribution, Multivariate additive Chi-square distribution and Multivariate additive Erlang-k distribution. Moreover, it is found that the bivariate correlation between two Nagakami random variables purely depends on the shape parameter and we simulated and established selected standard bivariate Nagakami correlation bounds from 2500 different combinations of values for shape parameter.

Key concepts: Multivariate stable distribution, Multivariate statistics, Mathematics, Multivariate t-distribution, Matrix t-distribution, Inverse-Wishart distribution, Normal-Wishart distribution, Wishart distribution

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