$$\lambda_1 = \beta_0 \mbox{,}$$ $$\lambda_2 = 2\beta_1 - \beta_0 \mbox{,}$$ $$\lambda_3 = 6\beta_2 - 6\beta_1 + \beta_0 \mbox{,}$$ $$\lambda_4 = 20\beta_3 - 30\beta_2 + 12\beta_1 - \beta_0 \mbox{,}$$ $$\lambda_5 = 70\beta_4 - 140\beta_3 + 90\beta_2 - 20\beta_1 + \beta_0 \mbox{,}$$ $$\tau = \lambda_2/\lambda_1 \mbox{,}$$ $$\tau_3 = \lambda_3/\lambda_2 \mbox{,}$$ $$\tau_4 = \lambda_4/\lambda_2 \mbox{, and}$$ $$\tau_5 = \lambda_5/\lambda_2 \mbox{.}$$
pwm2lmom(pwm)
pwm.ub
or similar.list
is returned.Hosking, J.R.M., 1990, L-moments--Analysis and estimation of distributions using linear combinations of order statistics: Journal of the Royal Statistical Society, Series B, vol. 52, p. 105--124.
Hosking, J.R.M., 1996, FORTRAN routines for use with the method of L-moments: Version 3, IBM Research Report RC20525, T.J. Watson Research Center, Yorktown Heights, New York.
Hosking, J.R.M. and Wallis, J.R., 1997, Regional frequency analysis---An approach based on L-moments: Cambridge University Press.
lmom.ub
, pwm.ub
, lmom2pwm
lmom <- pwm2lmom(pwm.ub(c(123,34,4,654,37,78)))
pwm2lmom(pwm.ub(rnorm(100)))
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