# MvtBurr: Multivariate Burr 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 multivariate Burr distribution with a scalar parameter parm1 and vectors of parameters parm2 and parm3.

## Usage

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 dmvburr(x, parm1 = 1, parm2 = rep(1, k), parm3 = rep(1, k), log = FALSE) pmvburr(q, parm1 = 1, parm2 = rep(1, k), parm3 = rep(1, k)) qmvburr( p, parm1 = 1, parm2 = rep(1, k), parm3 = rep(1, k), interval = c(0, 1e+08) ) rmvburr(n, parm1 = 1, parm2 = rep(1, k), parm3 = rep(1, k)) smvburr(q, parm1 = 1, parm2 = rep(1, k), parm3 = 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 d_i in Details. parm3 a vector of parameters, see parameters c_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(0, 1e8). n number of observations. k dimension of data or number of variates.

## Details

Multivariate Burr distribution (Johnson and Kotz, 1972) is a joint distribution of positive random variables X_1, \cdots, X_k. Its probability density is given as

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

where x_i >0, a,c_i,d_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( 1+∑_{i=1}^{k} d_i x_i^{c_i} \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 multivariate Burr distribution can be generated through transformation of multivariate Lomax random variables Y_1, \cdots, Y_k by letting X_i=(θ_i Y_i/d_i)^{1/c_i}, i = 1, \cdots, k; see Nayak (1987).

## Value

dmvburr gives the numerical values of the probability density.

pmvburr gives the cumulative probability.

qmvburr gives the equicoordinate quantile.

rmvburr generates random numbers.

smvburr gives the value of survival function.

## References

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

Johnson, N. L. and Kotz, S. (1972). Distribution in Statistics: Continuous Multivariate Distributions. New York: John Wiley & Sons, INC.

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 multivariate Burr with parameters: # a = 3, d1 = 1, d2 = 3, d3 = 5, c1 = 2, c2 = 4, c3 = 6 # Vector of quantiles: c(3, 2, 1) dmvburr(x = c(3, 2, 1), parm1 = 3, parm2 = c(1, 3, 5), parm3 = c(2, 4, 6)) # Density pmvburr(q = c(3, 2, 1), parm1 = 3, parm2 = c(1, 3, 5), parm3 = c(2, 4, 6)) # Cumulative Probability # Equicoordinate quantile of cumulative probability 0.5 qmvburr(p = 0.5, parm1 = 3, parm2 = c(1, 3, 5), parm3 = c(2, 4, 6)) # Random numbers generation with sample size 100 rmvburr(n = 100, parm1 = 3, parm2 = c(1, 3, 5), parm3 = c(2, 4, 6)) smvburr(q = c(3, 2, 1), parm1 = 3, parm2 = c(1, 3, 5), parm3 = c(2, 4, 6)) # Survival function