# MvtMardiaPareto1: Mardia's Multivariate Pareto Type I Distribution In NonNorMvtDist: Multivariate Lomax (Pareto Type II) and Its Related Distributions

## Description

Calculation of density function, cumulative distribution function, equicoordinate quantile function and survival function, and random numbers generation for Mardia's multivariate Pareto Type I distribution with a scalar parameter parm1 and a vector of parameters parm2.

## Usage

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 dmvmpareto1(x, parm1 = 1, parm2 = rep(1, k), log = FALSE) pmvmpareto1(q, parm1 = 1, parm2 = rep(1, k)) qmvmpareto1( p, parm1 = 1, parm2 = rep(1, k), interval = c(max(1/parm2) + 1e-08, 1e+08) ) rmvmpareto1(n, parm1 = 1, parm2 = rep(1, k)) smvmpareto1(q, parm1 = 1, parm2 = rep(1, k)) 

## Arguments

 x vector or matrix of quantiles. If x is a matrix, each row vector constitutes a vector of quantiles for which the density f(x) is calculated (for i-th row x_i, f(x_i) is reported). parm1 a scalar parameter, see parameter a in Details. parm2 a vector of parameters, see parameters θ_i in Details. log logical; if TRUE, probability densities f are given as log(f). q a vector of quantiles. p a scalar value corresponding to probability. interval a vector containing the end-points of the interval to be searched. Default value is set as c(max(1 / parm2) + 1e-8, 1e8) according to x_i > 1 / θ_i, θ_i>0, i=1,\cdots, k. n number of observations. k dimension of data or number of variates.

## Details

Multivariate Pareto type I distribution was introduced by Mardia (1962) as a joint probability distribution of several nonnegative random variables X_1, \cdots, X_k. Its probability density function is given by

f(x_1, \cdots, x_k) = \frac{[ ∏_{i=1}^{k} θ_i] a(a+1) \cdots (a+k-1)}{(∑_{i=1}^{k} θ_i x_i - k + 1)^{a+k}},

where x_i > 1 / θ_i, a >0, θ_i>0, i=1,\cdots, k.

Cumulative distribution function F(x_1, …, x_k) is obtained by the following formula related to survival function \bar{F}(x_1, …, x_k) (Joe, 1997)

F(x_1, …, x_k) = 1 + ∑_{S \in \mathcal{S}} (-1)^{|S|} \bar{F}_S(x_j, j \in S),

where the survival function is given by

\bar{F}(x_1, \cdots, x_k) = ≤ft( ∑_{i=1}^{k} θ_i x_i - k + 1 \right)^{-a}.

Equicoordinate quantile is obtained by solving the following equation for q through the built-in one dimension root finding function uniroot:

\int_{0}^{q} \cdots \int_{0}^{q} f(x_1, \cdots, x_k) dx_k \cdots dx_1 = p,

where p is the given cumulative probability.

Random numbers X_1, \cdots, X_k from Mardia's multivariate Pareto type I distribution can be generated through linear transformation of multivariate Lomax random variables Y_1, \cdots, Y_k by letting X_i = Y_i + 1/θ_i, i = 1, \cdots, k; see Nayak (1987).

## Value

dmvmpareto1 gives the numerical values of the probability density.

pmvmpareto1 gives the cumulative probability.

qmvmpareto1 gives the equicoordinate quantile.

rmvmpareto1 generates random numbers.

smvmpareto1 gives the value of survival function.

## References

Joe, H. (1997). Multivariate Models and Dependence Concepts. London: Chapman & Hall.

Mardia, K. V. (1962). Multivariate Pareto distributions. Ann. Math. Statist. 33, 1008-1015.

Nayak, T. K. (1987). Multivariate Lomax Distribution: Properties and Usefulness in Reliability Theory. Journal of Applied Probability, Vol. 24, No. 1, 170-177.

uniroot for one dimensional root (zero) finding.
  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # Calculations for the Mardia's multivariate Pareto Type I with parameters: # a = 5, theta1 = 1, theta2 = 2, theta3 = 3 # Vector of quantiles: c(2, 1, 1) dmvmpareto1(x = c(2, 1, 1), parm1 = 5, parm2 = c(1, 2, 3)) # Density pmvmpareto1(q = c(2, 1, 1), parm1 = 5, parm2 = c(1, 2, 3)) # Cumulative Probability # Equicoordinate quantile of cumulative probability 0.5 qmvmpareto1(p = 0.5, parm1 = 5, parm2 = c(1, 2, 3)) # Random numbers generation with sample size 100 rmvmpareto1(n = 100, parm1 = 5, parm2 = c(1, 2, 3)) smvmpareto1(q = c(2, 1, 1), parm1 = 5, parm2 = c(1, 2, 3)) # Survival function