CensMFM (version 1.5)

rMSN: Generating from Multivariate Skew-normal and Normal Random Distributions.

Description

It generates random realizations from a multivariate Skew-normal and Normal distribution.

Usage

rMSN(n, mu, Sigma, shape)

Arguments

n

number of observations.

mu

a numeric vector of length \(p\) representing the location parameter.

Sigma

a numeric positive definite matrix with dimension \(p\)x\(p\) representing the scale parameter.

shape

a numeric vector of length \(p\) representing the skewness parameter for Skew-normal(SN) case. If shape == 0, the SN case reduces to a normal (symmetric) distribution.

Value

It returns a \(n\) x \(p\) matrix containing the generated random realizations.

References

Cabral, C. R. B., Lachos, V. H., & Prates, M. O. (2012). Multivariate mixture modeling using skew-normal independent distributions. Computational Statistics & Data Analysis, 56(1), 126-142.

Prates, M. O., Lachos, V. H., & Cabral, C. (2013). mixsmsn: Fitting finite mixture of scale mixture of skew-normal distributions. Journal of Statistical Software, 54(12), 1-20.

C.E. Galarza, L.A. Matos, D.K. Dey & V.H. Lachos. (2019) On Moments of Folded and Truncated Multivariate Extended Skew-Normal Distributions. Technical report. ID 19-14. University of Connecticut.

F.H.C. de Alencar, C.E. Galarza, L.A. Matos & V.H. Lachos. (2019) Finite Mixture Modeling of Censored and Missing Data Using the Multivariate Skew-Normal Distribution. echnical report. ID 19-31. University of Connecticut.

See Also

fit.FMMSNC, rMMSN and rMMSN.contour

Examples

# NOT RUN {
mu     <- c(-3,-4)
Sigma  <- matrix(c(3,1,1,4.5), 2,2)
shape <- c(-3,2)
rMSN(10,mu = mu,Sigma = Sigma,shape = shape)
# }