Description Usage Arguments Value Author(s) Examples
Lévy distribution is characterized by the following probability density function,
f(x;μ,c) = √{\frac{c}{2π}} \frac{e^{-\frac{c}{2(x-μ)}}}{(x-μ)^{3/2}}
where the domain is x \in [μ,∞) with two parameters μ for location and c for scale.
1 |
x |
a length-n vector of values in [μ,∞). Due to its dependence on parameter μ, actual input can be any real-valued vector. |
weight |
a length-n weight vector. If set as |
a named list containing (weighted) MLE of
location parameter μ.
scale parameter c.
Kisung You
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # generate data from exponential distribution and shift
x = abs(stats::rexp(100)) + 2
# fit unweighted
Levy(x)
## Not run:
# put random weights to see effect of weights
niter = 500
ndata = 200
# generate data as above and fit unweighted MLE
x = abs(stats::rexp(ndata)) + 2
xmle = Levy(x)
# iterate
vec.mu = rep(0,niter)
vec.c = rep(0,niter)
for (i in 1:niter){
# random weight
ww = abs(stats::rnorm(ndata))
MLE = Levy(x, weight=ww)
vec.mu[i] = MLE$mu
vec.c[i] = MLE$c
if ((i%%10) == 0){
print(paste0(" iteration ",i,"/",niter," complete.."))
}
}
# distribution of weighted estimates + standard MLE
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
hist(vec.mu, main="location 'mu'")
abline(v=xmle$mu, lwd=3, col="red")
hist(vec.c, main="scale 'c'")
abline(v=xmle$c, lwd=3, col="blue")
par(opar)
## End(Not run)
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