lmomst3 | R Documentation |
This function estimates the first six L-moments of the 3-parameter Student t distribution given the parameters (\xi
, \alpha
, \nu
) from parst3
. The L-moments in terms of the parameters are
\lambda_1 = \xi\mbox{,}
\lambda_2 = 2^{6-4\nu}\pi\alpha\nu^{1/2}\,\Gamma(2\nu-2)/[\Gamma(\frac{1}{2}\nu)]^4\mbox{\, and}
\tau_4 = \frac{15}{2} \frac{\Gamma(\nu)}{\Gamma(\frac{1}{2})\Gamma(\nu - \frac{1}{2})} \int_0^1 \! \frac{(1-x)^{\nu - 3/2}[I_x(\frac{1}{2},\frac{1}{2}\nu)]^2}{\sqrt{x}}\; \mathrm{d} x - \frac{3}{2}\mbox{,}
where I_x(\frac{1}{2}, \frac{1}{2}\nu)
is the cumulative distribution function of the Beta distribution. The distribution is symmetrical so that \tau_r = 0
for odd values of r: r \ge 3
.
The functional relation \tau_4(\nu)
was solved numerically and a polynomial approximation made. The polynomial in turn with a root-solver is used to solve \nu(\tau_4)
in parst3
. The other two parameters are readily solved for when \nu
is available. The polynomial based on \log{\tau_4}
and \log{\nu}
has nine coefficients with a residual standard error (in natural logarithm units of \tau_4
) of 0.0001565 for 3250 degrees of freedom and an adjusted R-squared of 1. A polynomial approximation is used to estimate the \tau_6
as a function of \tau_4
; the polynomial was based on the theoLmoms
estimating \tau_4
and \tau_6
. The \tau_6
polynomial has nine coefficients with a residual standard error units of \tau_6
of 1.791e-06 for 3593 degrees of freedom and an adjusted R-squared of 1.
lmomst3(para, bypoly=TRUE)
para |
The parameters of the distribution. |
bypoly |
A logical as to whether a polynomial approximation of |
An R list
is returned.
lambdas |
Vector of the L-moments. First element is
|
ratios |
Vector of the L-moment ratios. Second element is
|
trim |
Level of symmetrical trimming used in the computation, which is |
leftrim |
Level of left-tail trimming used in the computation, which is |
rightrim |
Level of right-tail trimming used in the computation, which is |
source |
An attribute identifying the computational source of the L-moments: “lmomst3”. |
W.H. Asquith with A.R. Biessen
Asquith, W.H., 2011, Distributional analysis with L-moment statistics using the R environment for statistical computing: Createspace Independent Publishing Platform, ISBN 978–146350841–8.
parst3
, cdfst3
, pdfst3
, quast3
lmomst3(vec2par(c(1124,12.123,10), type="st3"))
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