For the hist estimate>par(mex=1.3)
>dens<-density(q)
>options(scipen=4)
> ylim<-range(dens$y)
> h<-hist(q,breaks="scott",freq=FALSE,probability=TRUE,
+? right=FALSE,xlim=c(9000,16000),ylim=ylim,main="Histogram of
q(scott)")> lines(dens)
>box()
?
For the kernel estimate>options(scipen=4)> d <- density(q, bw = "nrd0",kernel="gaussian")
> d
> plot(d)
?
In fact the variable q is a vector of 1000 simulated values; that is I generated
1000 samples from the pareto distribution, from each sample I calculated the
value of q ( a certain fn in the sample observations), and thus I was left with
1000 values of q and I don't know the distribution of q.
Hence, I used the attached codes for histogram and kernel density estimation
toestimate the density of q.
But what I'm really intersed in is to?estimate the probability that q is
greater than a certain value , for ex.,P(q>11000), using the density
estimates I obtained.
?Could u help me with a fn or some document to do this?
Thank u so much
Maram
? ? ??
Dear All,
Attached are the codes of a histogram & a kernel density estimate and the
output they produced. I'll copy the codes here in case there's something
wrong with the attachement
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