dprmvEST | R Documentation |
These functions provide the density function, probabilities and a random number
generator for the multivariate extended-skew t (EST) distribution with mean vector mu
, scale matrix Sigma
, skewness parameter lambda
, extension parameter tau
and degrees of freedom nu
.
dmvEST(x,mu=rep(0,length(lambda)),Sigma=diag(length(lambda)),lambda,tau=0,nu) pmvEST(lower = rep(-Inf,length(lambda)),upper=rep(Inf,length(lambda)), mu = rep(0,length(lambda)),Sigma,lambda,tau,nu,log2 = FALSE) rmvEST(n,mu=rep(0,length(lambda)),Sigma=diag(length(lambda)),lambda,tau,nu)
x |
vector or matrix of quantiles. If |
n |
number of observations. |
lower |
the vector of lower limits of length p. |
upper |
the vector of upper limits of length p. |
mu |
a numeric vector of length p representing the location parameter. |
Sigma |
a numeric positive definite matrix with dimension pxp representing the scale parameter. |
lambda |
a numeric vector of length p representing the skewness parameter for ST and EST cases. If |
tau |
It represents the extension parameter for the EST distribution. If |
nu |
It represents the degrees of freedom of the Student's t-distribution. |
log2 |
a boolean variable, indicating if the log2 result should be returned. This is useful when the true probability is too small for the machine precision. |
dmvEST
gives the density, pmvEST
gives the distribution function, and rmvEST
generates random deviates for the Multivariate Extended-Skew-t Distribution.
Christian E. Galarza <cgalarza88@gmail.com> and Victor H. Lachos <hlachos@uconn.edu>
Maintainer: Christian E. Galarza <cgalarza88@gmail.com>
Galarza, C. E., Lin, T. I., Wang, W. L., & Lachos, V. H. (2021). On moments of folded and truncated multivariate Student-t distributions based on recurrence relations. Metrika, 84(6), 825-850 <doi:10.1007/s00184-020-00802-1>.
Galarza, C. E., Matos, L. A., Castro, L. M., & Lachos, V. H. (2022b). Moments of the doubly truncated selection elliptical distributions with emphasis on the unified multivariate skew-t distribution. Journal of Multivariate Analysis, 189, 104944 <doi:10.1016/j.jmva.2021.104944>.
Genz, A., (1992) "Numerical computation of multivariate normal probabilities," Journal of Computational and Graphical Statistics, 1, 141-149 <doi:10.1080/10618600.1992.10477010>.
dmvST
, pmvST
, rmvST
, meanvarFMD
,meanvarTMD
,momentsTMD
#Univariate case dmvEST(x = -1,mu = 2,Sigma = 5,lambda = -2,tau = 0.5,nu=4) rmvEST(n = 100,mu = 2,Sigma = 5,lambda = -2,tau = 0.5,nu=4) #Multivariate case mu = c(0.1,0.2,0.3,0.4) Sigma = matrix(data = c(1,0.2,0.3,0.1,0.2,1,0.4,-0.1,0.3,0.4,1,0.2,0.1,-0.1,0.2,1), nrow = length(mu),ncol = length(mu),byrow = TRUE) lambda = c(-2,0,1,2) tau = 2 #One observation dmvEST(x = c(-2,-1,0,1),mu,Sigma,lambda,tau,nu=4) rmvEST(n = 100,mu,Sigma,lambda,tau,nu=4) #Many observations as matrix x = matrix(rnorm(4*10),ncol = 4,byrow = TRUE) dmvEST(x = x,mu,Sigma,lambda,tau,nu=4) lower = rep(-Inf,4) upper = c(-1,0,2,5) pmvEST(lower,upper,mu,Sigma,lambda,tau,nu=4)
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