gb (version 2.3.3)

gboot: Generalized bootstrapping

Description

Generalized bootstrapping

Usage

gboot(x,gldobj,statistic,...)

Arguments

x

A random sample.

gldobj

Either an object fitting a GLD or EGLD to data 'x'.

statistic

User defined function to resample from 'x'. 'fun' could be parametric or non-parametric.

...

Controls

References

Wang, B., Mishra, S.N., Mulekar, M., Mishra, N.S., Huang, K., (2010). Generalized Bootstrap Confidence Intervals for High Quantiles, In: Karian ZA, Dudewicz, EJ eds. The Handbook on Fitting Statistical Distributions with R. CRC Press. 2010: 877-913.

Wang, B., Mishra, S.N., Mulekar, M., Mishra, N.S., Huang, K., (2010). Comparison of bootstrap and generalized bootstrap methods for estimating high quantiles, Journal of Statistical Planning and Inferences, 140. 2926-2935. DOI: 10.1016/j.jspi.2010.03.016.

Karian, Z.A., Dudewicz, E.J., McDonald, P., 1996. The Extended Generalized Lambda Distribution System for Fitting Distributions to Data: history,completion of theory, tables, applications, the ``final word'' on moment fits, Comm. in Statist.- Simul. \& Comput. 25(3), 611-642.

Karian, Z.A., Dudewicz, E.J., 2000. Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods, Chapman and Hall/CRC.

Dudewicz, E.J., 1992. The Generalized Bootstrap, Bootstrapping and Related Techniques, In: K.H., G. Rothe, W. Sendler, eds., V. 376 of Lecture Notes in Economics and Mathematical Systems, Springer-Verlag, Berlin, 31-37.

Examples

# NOT RUN {
data(ofc)
X = ofc$x0
Ta = function(x) mean(x<31)
gld0 = fit.gld(X)
(out = gboot(X,gld0,statistic=Ta,R=100))
gld1 = fit.egld(X)
(out = gboot(X,gld1,statistic=Ta,R=100))

# }