Robust Asymmetric Distribution Parameter Estimates

DESCRIPTION:

Robust parameter estimation for gamma, lognormal, and Weibull distributions.

USAGE:

gammaRob(data, estim = c("tdmean", "M"), save.data = T,
control = gammaRob.control(estim, ...), ...)
lognormRob(data, estim = c("tdmean"), save.data = T,
control = lognormRob.control(estim, ...), ...)
weibullRob(data, estim = c("tdmean", "M"), save.data = T,
control = weibullRob.control(estim, ...), ...)

REQUIRED ARGUMENTS:

data
a vector of positive real numbers.

OPTIONAL ARGUMENTS:

estim
the robust estimator. The choices are "tdmean" for the truncated mean and "M" for the M. The M estimator is not available for the lognormal distribution.
save.data
if TRUE, the data is saved in the returned object.
control
a list of control parameters for the robust estimator. Use the links in the SEE ALSO section to see the parameters and their default settings.
...
control parameters may also be passed directly.

VALUE:

an asymmetric.dstn object with class "gammaRob", "lognormRob", or
"weibullRob" (respectively) containing the following components.
call
an image of the call that produced the object, but with the arguments all named.
mu
a robust estimate of the mean.
alpha
a robust estimate of the shape parameter.
sigma
a robust estimate of the scale parameter.
V.mu
a robust estimate of the variance of mu.
data
the data if data = T.
ok
equal to 1 if all numerical procedures exit cleanly.
The following elements are included for "tdmean" estimates.
Tl
the lower truncation point.
Tu
the upper truncation point.
The following element is included for "M" estimates.
cov
an estimate of the covariance of alpha and sigma.

DETAILS:

The classes "gammaRob", "lognormRob", and "weibullRob" are subclasses of "asymmetric.dstn". The generic methods are defined for class "asymmetric.dstn".

REFERENCES:

Marazzi A., Ruffieux C. (1999). The truncated mean of an asymmetric distribution. Computational Statististic and Data Analysis, 32, pp. 79-100.
Hampel F.R., Ronchetti E.M., Rousseeuw P.J., Stahel W.A. (1986). Robust statistics: the approach based on influence functions. Wiley, New York.
Marazzi A., Ruffieux C. (1996). Implementing M-estimators of the Gamma distribution. In: Rieder H. (Ed.), Robust Statistics, Data Analysis, and Computer intensive Methods, Springer Verlag.

SEE ALSO:

, , , , .

EXAMPLES:

los.gammarob <- gammaRob(los)
los.lognormrob <- lognormRob(los)
los.weibullrob <- weibullRob(los)