Description Usage Arguments Value Author(s) Examples
Dagum is characterized by the following probability density function,
f(x;p,a,b) = \frac{ap}{x} ≤ft( \frac{(x/b)^{ap}}{ ≤ft( (x/b)^a + 1 \right)^{p+1} } \right)
where the domain is x \in (0,∞) with two parameters p,a > 0 for shape and one parameter b for scale.
1 |
x |
a length-n vector of values in (0,∞). |
weight |
a length-n weight vector. If set as |
a named list containing (weighted) MLE of
shape parameter p.
shape parameter a.
scale parameter b.
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 41 | # generate data from half normal
x = abs(stats::rnorm(100))
# fit unweighted
Dagum(x)
## Not run:
# put random weights to see effect of weights
niter = 496
ndata = 200
# generate data as above and fit unweighted MLE
x = abs(stats::rnorm(ndata))
xmle = Dagum(x)
# iterate
vec.p = rep(0,niter)
vec.a = rep(0,niter)
vec.b = rep(0,niter)
for (i in 1:niter){
# random weight
ww = abs(stats::rnorm(ndata))
MLE = Dagum(x, weight=ww)
vec.p[i] = MLE$p
vec.a[i] = MLE$a
vec.b[i] = MLE$b
if ((i%%10) == 0){
print(paste0(" iteration ",i,"/",niter," complete.."))
}
}
# distribution of weighted estimates + standard MLE
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
hist(vec.p, main="shape 'p'"); abline(v=xmle$p,lwd=3,col="red")
hist(vec.a, main="shape 'a'"); abline(v=xmle$a,lwd=3,col="blue")
hist(vec.b, main="shape 'b'"); abline(v=xmle$b,lwd=3,col="green")
par(opar)
## End(Not run)
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