Asymmetric Multivariate Laplace Distribution

Description

These functions provide the density and random generation for the asymmetric multivariate Laplace distribution with location and skew parameter mu and covariance Sigma.

Usage

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daml(x, mu, Sigma, log=FALSE)
raml(n, mu, Sigma)

Arguments

x

This is a N x K matrix of data, or a vector of length K.

n

This is the number of observations, which must be a positive integer that has length 1.

mu

This is the location and skew parameter mu. This may be a N x K matrix, or a vector of length K.

Sigma

This is the K x K positive-definite covariance matrix Sigma.

log

Logical. If log=TRUE, then the logarithm of the density is returned.

Details

  • Application: Continuous Multivariate

  • Density: p(theta) = ((2exp(theta Omega theta)) / ((2pi)^(k/2)|Sigma|^(0.5))) ((theta Omega theta) / (2 + mu Omega mu))^((k-2)/4) K[(2-k)/2](sqrt((2 + mu Omega mu)(theta Omega theta)))

  • Inventor: Kotz, Kozubowski, and Podgorski (2003)

  • Notation 1: theta ~ AL[K](mu, Sigma)

  • Notation 2: p(theta) = AL[K](theta | mu, Sigma)

  • Parameter 1: location-skew parameter mu

  • Parameter 2: positive-definite covariance matrix Sigma

  • Mean: Unknown

  • Variance: Unknown

  • Mode: mode(theta) = mu

The asymmetric multivariate Laplace distribution of Kotz, Kozubowski, and Podgorski (2003) is a multivariate extension of the univariate, asymmetric Laplace distribution. It is parameterized according to two parameters: location-skew parameter mu and positive-definite covariance matrix Sigma. Location and skew occur in the same parameter. When mu=0, the density is the (symmetric) multivariate Laplace of Anderson (1992). As each location deviates from zero, the marginal distribution becomes more skewed. Since location and skew are combined, it is appropriate for zero-centered variables, such as a matrix of centered and scaled dependent variables in cluster analysis, factor analysis, multivariate regression, or multivariate time-series.

The asymmetric multivariate Laplace distribution is also discussed earlier in Kozubowski and Podgorski (2001), and is well-suited for financial modeling via multivariate regression, specifically with currency exchange rates. Cajigas and Urga (2005) fit residuals in a multivariate GARCH model with the asymmetric multivariate Laplace distribution, regarding stocks and bonds. They find that it "overwhelmingly outperforms" normality.

Value

daml gives the density, and raml generates random deviates.

References

Anderson, D.N. (1992). "A Multivariate Linnik Distribution". Statistical Probability Letters, 14, p. 333–336.

Cajigas, J.P. and Urga, G. (2005) "Dynamic Conditional Correlation Models with Asymmetric Laplace Innovations". Centre for Economic Analysis: Cass Business School.

Kotz, S., Kozubowski, T.J., and Podgorski, K. (2003). "An Asymmetric Multivariate Laplace Distribution". Working Paper.

Kozubowski, T.J. and Podgorski, K. (2001). "Asymmetric Laplace Laws and Modeling Financial Data". Mathematical and Computer Modelling, 34, p. 1003–1021.

See Also

dalaplace and dmvl

Examples

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library(LaplacesDemon)
x <- daml(c(1,2,3), c(0,1,2), diag(3))
X <- raml(1000, c(0,1,2), diag(3))
joint.density.plot(X[,1], X[,2], color=FALSE)

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