lmomkmu | R Documentation |
This function estimates the L-moments of the Kappa-Mu (\kappa:\mu
) distribution given the parameters (\nu
and \alpha
) from parkmu
. The L-moments in terms of the parameters are complex. They are computed here by the \alpha_r
probability-weighted moments in terms of the Marcum Q-function (see cdfkmu
). The linear combination relating the L-moments to the \beta_r
probability-weighted moments is
\lambda_{r+1} = \sum_{k=0}^{r} (-1)^{r-k} {r \choose k} { r + k \choose k } \beta_k
\mbox{,}
for r \ge 0
and the linear combination relating \alpha_r
to \beta_r
is
\alpha_r = \sum_{k=0}^r (-1)^k { r \choose k } \beta_k
\mbox{,}
and by definition the \alpha_r
are the expectations
\alpha_r \equiv E\{ X\,[1-F(X)]^r\}
\mbox{,}
and thus
\alpha_r = \int_{-\infty}^{\infty} x\, [1 - F(x)]^r f(x)\; \mathrm{d}x
\mbox{,}
in terms of x
, the PDF f(x)
, and the CDF F(x)
. Lastly, the \alpha_r
for the Kappa-Mu distribution with substitutions of the Marcum Q-function are
\alpha_r = \int_{-\infty}^{\infty} Q_\mu\biggl(\sqrt{2\kappa\mu},\; x\sqrt{2(1+\kappa)\mu}\biggr)^r\,x\, f(x)\; \mathrm{d}x\mbox{.}
Although multiple methods for Marcum Q-function computation are in cdfkmu
and discussed in that documentation, the lmomkmu
presenting is built only using the “chisq” approach.
Yacoub (2007, eq. 5) provides an expectation for the j
th moment of the distribution as given by
\mathrm{E}(x^j) = \frac{\Gamma(\mu+j/2)\mathrm{exp}(-\kappa\mu)}{\Gamma(\mu)[(1+\kappa)\mu]^{j/2}} \times {}_1F_1(\mu+j/2; \mu; \kappa\mu)
\mbox{,}
where {}_1F_1(a;b;z)
is the confluent hypergeometric function of Abramowitz and Stegun (1972, eq. 13.1.2). The lmomkmu
function optionally solves for the mean (j=1
) using the above equation in conjunction with the mean as computed by the order statistic minimums. The {}_1F_1(a;b;z)
is defined as
{}_1F_1(a;b;z) = \sum_{i=0}^\infty \frac{a^{(i)}}{b^{(i)}}\frac{z^i}{n!}
\mbox{,}
where the notation a^{(n)}
represents “rising factorials” that are defined as a^{(0)} = 1
and a^{(n)} = a(a+1)(a+2)\ldots(a+n-1)
. The rising factorials are readily computed by a^{(n)} = \Gamma(n+1)/\Gamma(n)
without resorting to a series computation. Yacoub (2007, eq. 5) is used to compute the mean.
lmomkmu(para, nmom=5, paracheck=TRUE, tol=1E-6, maxn=100)
para |
The parameters of the distribution. |
nmom |
The number of moments to compute. |
paracheck |
A logical controlling whether the parameters and checked for validity. |
tol |
An absolute tolerance term for series convergence of the confluent hypergeometric function when the Yacoub (2007) mean is to be computed. |
maxn |
The maximum number of interations in the series of the confluent hypergeometric function when the Yacoub (2007) mean is to be computed. |
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: “lmomkmu”. |
yacoubsmean |
A list containing the mean, convergence error, and number of iterations in the series until convergence. |
W.H. Asquith
Yacoub, M.D., 2007, The kappa-mu distribution and the eta-mu distribution: IEEE Antennas and Propagation Magazine, v. 49, no. 1, pp. 68–81.
parkmu
, cdfkmu
, pdfkmu
, quakmu
kmu <- vec2par(c(1.19,2.3), type="kmu")
lmomkmu(kmu)
## Not run:
par <- vec2par(c(1.67, .5), type="kmu")
lmomkmu(par)$lambdas
cdf2lmoms(par, nmom=4)$lambdas
system.time(lmomkmu(par))
system.time(cdf2lmoms(par, nmom=4))
## End(Not run)
# See the examples under lmomemu() so visualize L-moment
# relations on the L-skew and L-kurtosis diagram
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