pemq | R Documentation |
Density, distribution function, quantile function, random generation, expectile function and expectile distribution function for a family of distributions for which expectiles and quantiles coincide.
pemq(z,ncp=0,s=1)
demq(z,ncp=0,s=1)
qemq(q,ncp=0,s=1)
remq(n,ncp=0,s=1)
eemq(asy,ncp=0,s=1)
peemq(e,ncp=0,s=1)
ncp |
non centrality parameter and mean of the distribution. |
s |
scaling parameter, has to be positive. |
z , e |
vector of quantiles / expectiles. |
q , asy |
vector of asymmetries / probabilities. |
n |
number of observations. If length(n) > 1, the length is taken to be the number required. |
This distribution has the cumulative distribution function:
F(x;ncp,s) = \frac{1}{2}(1 + sgn(\frac{x-ncp}{s}) \sqrt{1 - \frac{2}{2 + (\frac{x-ncp}{s})^2}})
and the density:
f(x;ncp,s) = \frac{1}{s}( \frac{1}{2 + (\frac{x-ncp}{s})^2} )^\frac{3}{2}
It has infinite variance, still can be scaled by the parameter s
.
It has mean ncp
.
In the canonical parameters it is equal to a students-t distribution with 2 degrees of freedom.
For s = \sqrt{2}
it is equal to a distribution introduced by Koenker(2005).
demq
gives the density, pemq
and peemq
give the distribution function,
qemq
gives the quantile function, eemq
computes the expectiles numerically and is only provided for completeness,
since the quantiles = expectiles can be determined analytically using qemq
,
and remq
generates random deviates.
Fabian Otto- Sobotka
Carl von Ossietzky University Oldenburg
https://uol.de
Thomas Kneib
Georg August University Goettingen
https://www.uni-goettingen.de
Koenker R (2005) Quantile Regression Cambridge University Press, New York
enorm
x <- seq(-5,5,length=100)
plot(x,demq(x))
plot(x,pemq(x,ncp=1))
z <- remq(100,s=sqrt(2))
plot(z)
y <- seq(0.02,0.98,0.2)
qemq(y)
eemq(y)
pemq(x) - peemq(x)
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