Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/GEVFamilyMuUnknown.R
Generates an object of class "GEVFamilyMuUnknown"
which
represents a Generalized EV family with unknown location parameter mu
.
1 2 3 4 5 | GEVFamilyMuUnknown(loc = 0, scale = 1, shape = 0.5, of.interest = c("loc",
"scale", "shape"), p = NULL, N = NULL, trafo = NULL,
start0Est = NULL, withPos = TRUE, secLevel = 0.7,
withCentL2 = FALSE, withL2derivDistr = FALSE, withMDE = FALSE,
..ignoreTrafo = FALSE, ..withWarningGEV = TRUE, ..name = "")
|
loc |
real: known/fixed threshold/location parameter |
scale |
positive real: scale parameter |
shape |
positive real: shape parameter |
of.interest |
character: which parameters, transformations are of interest. |
p |
real or NULL: probability needed for quantile and expected shortfall |
N |
real or NULL: expected frequency for expected loss |
trafo |
matrix or NULL: transformation of the parameter |
start0Est |
startEstimator — if |
withPos |
logical of length 1: Is shape restricted to positive values? |
secLevel |
a numeric of length 1: In the ideal GEV model, for each observastion Xi, the expression 1+shape(Xi-loc)/scale must be positive, which in principle could be attacked by a single outlier. Hence for sample size n we allow for eps n violations, interpreting the violations as outliers. Here eps = secLevel/sqrt(n). |
withCentL2 |
logical: shall L2 derivative be centered by substracting
the E()? Defaults to |
withL2derivDistr |
logical: shall the distribution of the L2 derivative
be computed? Defaults to |
withMDE |
logical: should Minimum Distance Estimators be used to
find a good starting value for the parameter search?
Defaults to |
..ignoreTrafo |
logical: only used internally in |
..withWarningGEV |
logical: shall warnings be issued if shape is large? |
..name |
character: optional alternative name for the parametric family; used in generating interpolating grids. |
The slots of the corresponding L2 differentiable parameteric family are filled.
Object of class "GEVFamilyMuUnknown"
Matthias Kohl Matthias.Kohl@stamats.de
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
Nataliya Horbenko nhorbenko@gmail.com
Kohl, M. (2005) Numerical Contributions to
the Asymptotic Theory of Robustness. Bayreuth: Dissertation.
M.~Kohl, P. Ruckdeschel, H.~Rieder (2010):
Infinitesimally Robust Estimation in General Smoothly Parametrized Models.
Stat. Methods Appl., 19, 333–354.
Ruckdeschel, P. and Horbenko, N. (2012): Yet another breakdown point notion: EFSBP –illustrated at scale-shape models. Metrika, 75(8), 1025–1047.
1 2 3 | (G1 <- GEVFamilyMuUnknown())
FisherInfo(G1)
checkL2deriv(G1)
|
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