Estimates the parameters of a normal inverse Gaussian distribution.
nigFit(x, alpha = 1, beta = 0, delta = 1, mu = 0, method = c("mle", "gmm", "mps", "vmps"), scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...)
alpha
, beta
, delta
, and
mu
:
shape parameter alpha
;
skewness parameter beta
, abs(beta)
is in the
range (0, alpha);
scale parameter delta
, delta
must be zero or
positive;
location parameter mu
, by default 0.
These is the meaning of the parameters in the first
parameterization pm=1
which is the default
parameterization selection.
In the second parameterization, pm=2
alpha
and beta
take the meaning of the shape parameters
(usually named) zeta
and rho
.
In the third parameterization, pm=3
alpha
and beta
take the meaning of the shape parameters
(usually named) xi
and chi
.
In the fourth parameterization, pm=4
alpha
and beta
take the meaning of the shape parameters
(usually named) a.bar
and b.bar
.
"mle"
, Maximum Likelihood Estimation, the default,
"gmm"
Gemeralized Method of Moments Estimation,
"mps"
Maximum Product Spacings Estimation, or
"vmps"
Minimum Variance Product Spacings Estimation.
TRUE
. Should the time series
be scaled by its standard deviation to achieve a more stable
optimization?
span=seq(min, max,
times = n)
, where, min
and max
are the
left and right endpoints of the range, and n
gives
the number of the intermediate points.
tFit
, hypFit
and nigFit
return
a list with the following components:estimate
.
Either estimate
is an approximate local minimum of the
function or steptol
is too small;
4: iteration limit exceeded;
5: maximum step size stepmax
exceeded five consecutive times.
Either the function is unbounded below, becomes asymptotic to a
finite value from above in some direction or stepmax
is too small.
## nigFit -
# Simulate Random Variates:
set.seed(1953)
s = rnig(n = 1000, alpha = 1.5, beta = 0.3, delta = 0.5, mu = -1.0)
## nigFit -
# Fit Parameters:
nigFit(s, alpha = 1, beta = 0, delta = 1, mu = mean(s), doplot = TRUE)
Run the code above in your browser using DataLab