Description Usage Arguments Details Value Author(s) References Examples
Estimates the parameters of an α-stable distribution using the quantile estimator of McCulloch (1986).
1 | mccullochquantile(x, symm=FALSE)
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x |
A numeric vector of data. Missing values (NA) are allowed, but will be removed during the calculation. |
symm |
Logical, TRUE to assume a symmetric α-stable distribution. Default is FALSE. |
Estimates the parameters of an α-stable distribution using McCulloch's quantile-based estimator. This estimator is fast but not as accurate as other approaches (e.g., maximum likelihood). It is useful as a starting value in more accurate (but more computationally intensive) estimates.
A vector of length 5 containing the parameter estimates (in this order):
alpha | the index of stability |
beta | the skewness parameter |
c | the scale parameter |
delta | the location parameter |
zeta | the shifted location parameter (of Zolotarev) |
Based on J. H. McCulloch's GAUSS implementation, which can be found on McCulloch's homepage: http://www.econ.ohio-state.edu/jhm/jhm.html .
S-PLUS/C code and R port by Christopher G. Green christopher.g.green@gmail.com.
J. Huston McCulloch, "Simple Consistent Estimators of Stable Distribution Parameters", Communications in Statistics, Simulation, 1986 (15:4), 1109-1136.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | mccullochquantile( rnorm(1000), symm=TRUE )
# test case from mcculloch's paper
require(stabledist) # for rstable
# test how well the estimate works for different sample sizes
results <- sapply(c(10, 100, 1000, 10000),
function(n) sapply(1:1000,
function(i,n) abs(mccullochquantile(rstable(n,1.45,0),symm=TRUE)$alpha - 1.45),
n=n))
colMeans(results)
apply(results, 2, sd)
# an asymmetric case
results <- sapply(c(10, 100, 1000, 10000),
function(n) sapply(1:1000,
function(i,n) abs(mccullochquantile(rstable(n,1.45,0.2))$beta - 0.2),
n=n))
colMeans(results)
apply(results, 2, sd)
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