NormalLaplaceMeanVar | R Documentation |
Functions to calculate the mean, variance, skewness and kurtosis of a specified normal Laplace distribution.
nlMean(mu = 0, sigma = 1, alpha = 1, beta = 1,
param = c(mu, sigma, alpha, beta))
nlVar(mu = 0, sigma = 1, alpha = 1, beta = 1,
param = c(mu, sigma, alpha, beta))
nlSkew(mu = 0, sigma = 1, alpha = 1, beta = 1,
param = c(mu, sigma, alpha, beta))
nlKurt(mu = 0, sigma = 1, alpha = 1, beta = 1,
param = c(mu, sigma, alpha, beta))
mu |
Location parameter |
sigma |
Scale parameter |
alpha |
Skewness parameter |
beta |
Shape parameter |
param |
Specifying the parameters as a vector of the form |
Users may either specify the values of the parameters individually or
as a vector. If both forms are specified, then the values specified by
the vector param
will overwrite the other ones.
The mean function is
E(Y)=\mu+1/\alpha-1/\beta.
The variance function is
V(Y)=\sigma^2+1/\alpha^2+1/\beta^2.%
The skewness function is
\Upsilon =
[2/\alpha^3-2/\beta^3]/[\sigma^2+1/\alpha^2+1/\beta^2]^{3/2}.%
The kurtosis function is
\Gamma = [6/\alpha^4 +
6/\beta^4]/[\sigma^2+1/\alpha^2+1/\beta^2]^2.
nlMean
gives the mean of the skew hyperbolic nlVar
the
variance, nlSkew
the skewness, and nlKurt
the kurtosis.
David Scott d.scott@auckland.ac.nz, Jason Shicong Fu
William J. Reed. (2006) The Normal-Laplace Distribution and Its Relatives. In Advances in Distribution Theory, Order Statistics and Inference, pp. 61–74. Birkhäuser, Boston.
param <- c(10,1,5,9)
nlMean(param = param)
nlVar(param = param)
nlSkew(param = param)
nlKurt(param = param)
curve(dnl(x, param = param), -10, 10)
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