davies.moment | R Documentation |
Moments of order statistics of random variables drawn from a Davies distribution
davies.moment(n=1 , i=1 , order=1 , params)
M(order,params)
mu(params)
expected.value(n,i,params)
expected.value.approx(n,i,params)
variance(params)
skewness(params)
kurtosis(params)
params |
A three-member vector holding |
n |
The number of observations |
i |
Return information about the |
order |
The order (eg order=2 gives the square) |
Function davies.moment(n,i,order=r)
gives the r
-th moment
of the i
-th order statistic of n
observations. The
following aliases are just convenience wrappers with n=i=1
(ie
moments of one observation from a Davies distribution):
M()
gives the r
-th moment for n=i=1
mu()
gives the first moment of a Davies distribution
(ie the mean)
variance()
gives the second central moment of a Davies
distribution
skewness()
gives the normalized skewness of a Davies
distribution
kurtosis()
gives the normalized kurtosis of a Davies
distribution
Robin K. S. Hankin
expected.value
, expected.gld
params <- c(10,0.1,0.1)
davies.moment(n=100,i=99,2,params) # ie the second moment of the 99th smallest
# observation of 100 drawn from a Davies
# distribution with parameters p
mean(rdavies(1e6,params))-mu(params)
#now reproduce the S-K graph:
f <- function(x,y){c(skewness(c(1,x,y)),kurtosis(c(1,x,y)))}
g <- function(j,vector,pp,qq=1){points(t(sapply(vector,f,y=j)),type="l",col="black",lty=qq)}
vector <- c((0:300)/100 , (0:300)/10000 , seq(from=3,to=10,len=100))
vector <- sort(unique(vector))
plot(t(sapply((0:10)/10,f,y=0)),
xlim=c(-3,3),ylim=c(0,10),
type="n",xlab="skewness",ylab="kurtosis")
g(0.001,vector,"red",qq=1)
g(0.01,vector,"yellow",qq=2)
g(0.02,vector,"green",qq=3)
g(0.05,vector,"blue",qq=4)
g(0.1 ,vector,"purple",qq=5)
g(0.14,vector,"black",qq=6)
x <- seq(from=-3,to=3,len=30)
points(x,x^2+1,type="l",lwd=2)
leg.txt <- expression(lambda[2]==0.001,
lambda[2]==0.01,lambda[2]==0.02,lambda[2]==0.05,
lambda[2]==0.1,lambda[2]==0.14)
legend(-1.1,10,leg.txt,col="black",lty=1:6)
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