dCornishFisher | R Documentation |
Density, distribution function, quantile function and random generation using Cornish-Fisher approximation.
dCornishFisher(x, n, skew, ekurt)
pCornishFisher(q, n, skew, ekurt)
qCornishFisher(p, n, skew, ekurt)
rCornishFisher(n, sigma, skew, ekurt, dp = NULL, seed = NULL)
x , q |
vector of standardized quantiles. |
n |
scalar; number of simulated values in random simulation, sample length in density, distribution and quantile functions. |
skew |
scalar; skewness. |
ekurt |
scalar; excess kurtosis. |
p |
vector of probabilities. |
sigma |
scalar standard deviation. |
dp |
a vector of length 3, whose elements represent sigma, skew and
ekurt, respectively. If dp is specified, the individual parameters cannot be
set. Default is |
seed |
scalar; set seed. Default is |
CDF(q) = Pr(sqrt(n)*(x_bar-mu)/sigma < q)
dCornishFisher
Computes Cornish-Fisher density from two term Edgeworth
expansion given mean, standard deviation, skewness and excess kurtosis.
pCornishFisher
Computes Cornish-Fisher CDF from two term Edgeworth
expansion given mean, standard deviation, skewness and excess kurtosis.
qCornishFisher
Computes Cornish-Fisher quantiles from two term
Edgeworth expansion given mean, standard deviation, skewness and excess
kurtosis.
rCornishFisher
simulates observations based on Cornish-Fisher quantile
expansion given mean, standard deviation, skewness and excess kurtosis.
dCornishFisher
gives the density, pCornishFisher
gives the
distribution function, qCornishFisher
gives the quantile function,
and rCornishFisher
generates n
random simulations.
Eric Zivot and Yi-An Chen.
DasGupta, A. (2008). Asymptotic theory of statistics and probability. Springer. Severini, T. A., (2000). Likelihood Methods in Statistics. Oxford University Press.
## Not run:
# generate 1000 observation from Cornish-Fisher distribution
rc <- rCornishFisher(1000,1,0,5)
hist(rc, breaks=100, freq=FALSE,
main="simulation of Cornish Fisher Distribution", xlim=c(-10,10))
lines(seq(-10,10,0.1), dnorm(seq(-10,10,0.1), mean=0, sd=1), col=2)
# compare with standard normal curve
# exponential example from A.dasGupta p.188
# x is iid exp(1) distribution, sample size = 5
# then x_bar is Gamma(shape=5, scale=1/5) distribution
q <- c(0,0.4,1,2)
# exact cdf
pgamma(q/sqrt(5)+1, shape=5, scale=1/5)
# use CLT
pnorm(q)
# use edgeworth expansion
pCornishFisher(q, n=5, skew=2, ekurt=6)
## End(Not run)
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