galton_moors2alpha_nu {sn} | R Documentation |
Mapping of the (Galton-Bowley, Moors) measures to the
(alpha
, nu
) parameters of a ST distribution
Description
Given a pair of (Galton-Bowley, Moors) measures of skewness and
kurtosis for a given sample, galton_moors2alpha_nu
delivers values
(alpha
, nu
) such that a skew-t (ST) distribution
with these slant and tail-weight parameter has its (Galton-Bowley, Moors)
measures equal to the input values.
Its simplified version galton2alpha
uses only a Galton-Bowley measure
to deliver a alpha
value, assuming a SN distribution.
These functions are mainly intended for internal package usage.
Usage
galton_moors2alpha_nu(galton, moors, quick = TRUE, move.in = TRUE, verbose = 0,
abstol = 1e-04)
galton2alpha(galton, move.in = TRUE)
Arguments
galton |
a numeric value, representing a Galton-Bowley measure |
moors |
a numeric value, representing a Moors measure |
quick |
a logical value; if |
move.in |
if the input values |
verbose |
a numeric value which regulates the amount of printed detail |
abstol |
the tolerance value of the mapping, only relevant is
|
Details
For background information about the Galton-Bowley's and
the Moors measures, see the documentation of fournum
.
The working of the mapping by described in Azzalini and Salehi (2020).
Value
for galton_moors2alpha_nu
, named vector of length two,
with one or more descriptive attributes;
for galton2alpha
, a single alpha
value.
Note
These functions are mainly intended for internal package usage.
Specifically they are used by st.prelimFit
.
Author(s)
Adelchi Azzalini
References
Azzalini, A. and Salehi, M. (2020). Some computational aspects of maximum likelihood estimation of the skew-t distribution. In: Computational and Methodological Statistics and Biostatistics, edited by Andriƫtte Bekker, Ding-Geng Chen and Johannes T. Ferreira. Springer. DOI: 10.1007/978-3-030-42196-0
See Also
Examples
galton_moors2alpha_nu(0.5, 3, quick=FALSE) # input in the feasible area
galton_moors2alpha_nu(0.5, 3) # very similar output, much more quickly
galton_moors2alpha_nu(0.5, 0.5) # input outside the feasible area